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There is not enough evidence to reject N2, which actually supports H2 more than what was initially predicted; an anti-establishment impact on voting patterns was expected but did not even come across in the data.
As N3 is strongly rejected, there is therefore evidence to argue in favour of focusing less on Donald Trump as the architect of his own success, and more on the impact that incumbency had on voting choices.
Obviously, I am not arguing that the outcome of the election would have been the same regardless of the candidate. Although these questions go beyond the scope of this paper, they would be interesting to explore further to continue growing the literature on American elections.
Each model also uses a number of variables intended to control for the influence of demographics on voting patterns. These variables include controlling for racial identity, annual income, gender, level of education, political affiliation, ideological standing and importance of religious faith. These variables were selected because they were found to skew voting patterns. This simplifies the diversity of the American electorate but given that a discrete numerical scale is not suitable for demographic variables, this binary approach is a suitable way to account for the influence of racial identity on voting patterns.
These control variables are used consistently across every model and the median demographic profile of respondents across the entire survey is used to calculate predicted probabilities see Table 2. As explained in the paper, there are three key assumptions that must be respected for a binary logistic regression model to yield reliable results.
Multicollinearity occurs when the independent variables of a model are highly correlated rather than independent from one another. This implies that a change in one variable will be associated with a shift in another variable. The stronger the correlation, the greater the change is—it therefore becomes impossible to estimate what actually causes the change in the dependent variable since both independent variables move in unison.
Thus, we need to test for non-multicollinearity making sure all are variables are independent , which we do through the use of the Variance Inflation Factor VIF —the VIF simply identifies the association between variables and the strength of that association. Linearity is slightly more complex—for a linear regression a different kind of model , the assumption states that the independent variables must have a linear relationship with the dependent variables. For a logistic regression, however, the assumption is formulated differently, since the outcome voting for Donald Trump is binary rather than continuous.
In this context, we are therefore assuming that there is a linear relationship between the independent variables and the log odds of the dependent variable. Finally, independent errors assumes that errors should not be correlated for different observations. This is a common issue in statistical research, especially when dealing with various hierarchical levels of clustering.
For example, if we were doing a study on student behaviour within a school, it is likely that the results will be impacted by the fact that students from the same class may behave similarly. This assumption is actually dealt with for us by the CCES, which design weights which indicate the probability with which an individual case was likely to be selected within the sample.
Explaining Odd Ratios and Predicted Probabilities:. In simple terms, it indicates the odds that an outcome will occur given a particular circumstance. For example, if my odds ratio is equal to 2, it means that there is twice the change of the outcome happening. If it is equal to 0. This can also be converted to percentages—if my odds ratio is equal to 2, then the outcome is percent more likely to occur based on specific circumstances.
Jones, Jeffrey. Obama Averages Gallup, 20 Jan. With the COVID pandemic, computer-generated models of social behaviour have gained unprecedented prominence. This article contends that although they have been used in policy for decades, the crisis could be a watershed moment.
The article argues that Report 9 and similar simulations must be seen as political actors, situated in wider economies of expertise, rather than merely apolitical tools.
They alter policies, ideologies and modes of government even as they are built to aid them. Jamie Hancock graduated from Peterhouse with a BA in Human, Social, and Political Sciences, focusing specifically on the study of sociology and social anthropology. I argue that Report 9 and similar simulations must be seen as political actors, situated in wider economies of expertise, rather than merely apolitical tools.
Introduction: contagion models and contentious politics. With the COVID pandemic, computer-generated models of social behaviour have gained unprecedented political prominence.
Though they have been in policy use for decades Gilbert et al , I contend that the crisis could be a watershed moment. Policymakers have come to rely on such simulations to a far greater extent than they did previously.
These are presented as aids in navigating uncertain futures and charting landscapes of risk. In the process, their models are likely to alter prevailing modes of government. These developments have been especially striking in the United Kingdom.
The history of the pandemic in Britain is in part that of the models which have been used to explain, graph, predict and pre-empt it. It is a history of possible presents and diverging futures — the multiple pandemics which might have occurred, may be occurring or could still be to come.
I intend to examine this history, centring the models themselves as political actors. Over the last two decades, complexity simulations have become crucial guides for pandemic forecasting, akin to their use for climate dynamics Donner et al ; Mercure et al or economic processes Arthur Yet these models do not merely provide insights into the world on which interventions can be based.
They construct worlds in which interventions can be made Mol And as forecasting tools derived from complex systems theory CST , they help enact speculative ontologies - claims about the nature of what is possible. In the process, their political roles have expanded. Report 9 provides a window to study the politics of social simulations in general. It means examining the theoretical premises on which it was based; the presumptions and oversights encoded in its model; the frameworks through which it was interpreted; and the kind of knowledge economy in which it was situated.
These conditions were not necessarily present in comparable countries. In this policy environment, updated input figures could output political shockwaves.
This was a necropolitics by omission: the omission of how morbidity and mortality rates are structured by race, class and other intersecting structures of inequality Randhawa ; Kelley et al In other words, it is colour-blind. Though the model itself may not have been biased, it helped enact outcomes which were highly unequal. Such a necropolitics was made possible by a more general politics of knowledge.
Thus, to frame data as apolitical is itself political. The ability to build, read and utilise simulations is predicated on political-economy where expertise forms a valuable resource — one which privileges governments, transnational institutions, universities and large corporations, generally in the post-imperial Global North Lansing It also emphasised the irreducibly relational nature of social life echoing Latour Each of these effects undermines a key tenet of neoliberal thought.
In other words, complexity models aid centralised governments in overcoming the information problems they face — the very kinds of problems neoliberal theory developed to critique. They can help interventionism make a comeback in conjunction with privatisation. New, localised restrictions are being imposed each day ibid. Epidemiological modellers such as Ferguson and his colleagues have again been at the centre of these developments ibid; Clark ; Slawson Social simulations appear to be here to stay.
March will likely prove a watershed in British politics. Epidemiological simulations were at the centre of this transition. In this section, I will situate these events and Report 9 in the policy environment from which they emerged. I ask: what happened to position this model to have such dramatic effects? Following initial reports from Chinese officials of a new form of pneumonia in the city of Wuhan on 31 December , Ferguson and his colleagues began work on a model to estimate the case-rate Imai et al a.
Their claims were based on the available population data for Wuhan, the volume of airport traffic and case-rates amongst international travellers.
Ferguson, for example, appeared in The Telegraph suggesting that the virus was a potential risk for the UK Newey In South Korea, a mass testing regime was developed rapidly — from a day in early February to up to 20, a day by the end of March Dighe et al A second outbreak was identified on 6 February Calvert et al Ministers made increasing use of this rhetorical frame as the crisis grew. But this evidence itself was uncertain Imai et al a, b; Ferguson et al There was not enough data available regarding incubation periods, infection rates, mortality rates or a host of other factors Calvert et al Retrospective analyses have pointed to the lack of mass testing as a key factor for example, Dighe et al Without the data gathered by such surveillance regimes, ministers would have perceived an ambiguous and risk-laden environment.
Modelling seems to have offered a high-tech way of mitigating such uncertainty. It stated that there was a large range of uncertainty regarding the transmissibility of COVID, though the infection rate was likely far higher than for influenza Imai et al c.
They did not adapt well to meet the conditions set by the new disease. Furthermore, modellers and their simulations can only work with the information available. This meant that when the inputted data was updated, the outputs could be radically different — something demonstrated already by the increase in projected cases between Reports 1 and 2.
Relatively minor alterations in the models could alter the very nature of the futures SAGE was presented with. Government publicity suggests they drove its first major action-plan published on 3 March BBC News a. This provided details of potential social distancing measures including school closures but made no mandatory requirements for the public. Meanwhile, all contact tracing was stopped. The priority appears to have been to project a sense of control and avoid challenging orthodoxies.
Ideological inflexibility seems to have been a particular problem. Mandatory social distancing and lockdown measures are the antithesis of laissez-faire government. It distracted from how the tools used for policymaking were selected by politicians and the flawed processes within which these tools were situated. They argue that the gap between 24 January and 2 March was lost time. We just watched.
This situation primed the results published in Report 9 to have their shocking effect when they appeared on 16 March. Furthermore, the fact that they were generated by a simulation afforded this data a reality that other scientific evidence was not. The ensuing policies and their impacts have been well documented elsewhere. First came stricter guidance on social distancing, as of 16 March.
Then on 23 March a full lockdown policy came into force, with tight governmental controls on businesses, travel and socialising Geskell et al ; HM Cabinet Office In turn, ministers rushed to propose measures to alleviate the resulting economic fallout.
Reading these policies presents an image of a unified Britain which would experience the lockdown together. National newspapers profiled scientists from fields such as computational neuroscience who have applied their experience to modelling the pandemic for example, Spinney Numerous stories have focused on either alternative models or alternative points in time at which they should have made their lockdown decision such as Macdonald ; Ward ; Perrigo Therefore, I suggest 16 March marks a turning point at which an awareness of social simulations as practical policy tools emerged in British culture.
Social simulations do not appear to have played such a decisive role in comparable countries. By then, Netherlanders had tested positive Shaart Moreover, there was greater transparency regarding the data informing these decisions Enserink and Kupferschmidt New Zealand operated with perhaps even more openness — both regarding their sources and the uncertainties in the science upon which they relied.
Its lockdown began on 25 March, when the country had only cases ibid. Social simulations present claims about the nature of both presents and futures. In other words, they provide speculative ontologies, projecting what might be. To use them, policymakers must either combine them with or prioritise them over other sources of data. Updating these models with newer and higher resolution data can lead to drastic revisions in what these futures look like.
Alterations to the futures presented to policymakers — in this case, SAGE and the British government — can open new conceptual horizons, as well as give shape to previously unknown dangers.
Crucially, an overreliance on simulations in a context where there is very little pre-existing information can amplify these epistemic instabilities and uncertainties. Why, in contrast with all the other reports and warnings, were these numbers able to alter this situation? I argue a more holistic explanation requires examining the numbers themselves, situating them in their theoretical, methodological and technological contexts. The data and the simulations which created them need to be centred as actors with life histories of their own.
Stated briefly, simulation modelling is not the same as statistical modelling nor modelling through machine learning techniques.
What I am calling social simulations are, technically, simulations of complex systems which use abstracted mathematical rules intended to represent those systems. Their key theoretical influence lies in complex systems theory CST. More generally, modelling describes a process where inputs are used to create an approximation of the behaviour of an intended target. Traditional statistical modelling takes a dataset and uses analytic techniques to discern relationships present between variables.
The aim might be to establish correlations, to make predictions, or to establish degrees of confidence about particular hypotheses. Historically, more traditional epidemiological modelling has worked this way Eubank et al Both modelling approaches rely on processing observed data to gain better knowledge about a system. In contrast, complexity modelling uses pre-established knowledge about a system to generate data by iterating on a set of rules. These are based on current theories about how a system works — for example, the rate of infection between individuals — and coded mathematically.
As variables are adjusted, many different outputs are possible, enabling researchers to explore different potential dynamics and provide degrees of confidence. Simulations are calibrated on pre-existing datasets to test whether they match expected results. The more accurate the ruleset and the more precise their input data is, the better they will do so Epstein Their process is almost the inverse of machine learning techniques: they utilise carefully chosen samples, sometimes fictional ones, by running them through known models to produce unpredictable results.
Indeed, massive surveillance has been a precondition for the emergence of all the above conceptualisations of Big Data Lyon Hence, Big Data should not be confused with specific modelling techniques. Nevertheless, Big Data sets , statistical inference, machine learning and simulations are usable together. Simulation techniques have an increased power when combined with massive datasets — most easily attainable through mass surveillance — and algorithmic optimisation.
Big Data analytics were presented as a panacea to the uncertainties resulting from a lack of testing capacity. The government attempted to utilise them by contracting the data processing company Palantir and machine-learning specialists Faculty AI for its first try at a COVID tracking app Lewis et al and later for a project analysing public opinion on Twitter Pegg This is because complexity models bring their own specific ontological and epistemic principles.
Ontologically, complex systems are relational. Hence, complexity provides a way of conceptualising a remarkable number of phenomena Holling CST has been applied in fields as disparate as neurology Cilliers ; Friston et al , practice theory Holtz and studies of traditional Balinese irrigation systems Lansing , It involves an ontology which is simultaneously relational, non-linear and de-materialised.
CST also presents a particular epistemology which can prove problematic for governmentalities. Complex systems are practically impossible to predict in the long term. Furthermore, full information about a given system is unattainable for any given moment, unless the model used is as complex as the system itself Cillers In turn, this means that a world seen in the frame of complexity theory is one characterised by uncertainty, risk and runaway effects Urry Interventions may be made into a complex system based on a theory of how it works — a mandatory lockdown, say, or quantitative easing measures — but the results will be ultimately unpredictable.
Complexity modelling offers a standpoint suited to governance and has been used for a wide array of policy initiatives Gilbert et al The most apt comparison pandemic simulations might be models for studying climate change — not least in terms of the biopolitics involved and the degree of publicity they have attracted Beck ; Mercure et al Often this is represented by a kind of view from above, with elements seen interacting on a grid or network.
In epidemiology, the use of simulation is relatively novel. Report 9 is an excellent example of an epidemiological complexity model developed for policy. These interactions lead to emergent contagion dynamics. Yet there is trade off: the more heterogenous and complex the system, the more computing power is required to run the simulation Eubank et al Report 9 was an almost archetypal case of a biopolitical technology — a tool to aid the proper management of life and health Foucault The model was seeded with high-resolution data from the census in addition to surveys on the distribution of workplace sizes, commuting distances, schools and similar hubs of social activity.
Furthermore, it required a theoretical and methodological scaffold, and a discursive framework to interpret it. It also involved a specific form of necropolitics: a necropolitics by omission. Government pandemic policies are about deciding trade-offs.
In other words, they are policies about expendability. Such a policy requires making decisions about the degree to which the government is willing to protect different groups and systems. This is overtly necropolitical policymaking. Such necropolitical overtones did not come across well. Because the framing now emphasised equality of access to life, this implied a turn to a more implicit necropolitics in Britain — but necropolitics nevertheless. As the instigating factor in the change of policy direction, it was positioned to have a unique influence.
Though this is necessarily speculative, it may be that — had it foregrounded data on health inequalities and emphasised the importance of equity in outcomes — the report could have helped promote policies which would have safeguarded those most vulnerable to complications resulting from COVID As outlined above, simulation involves significant trade-offs between representing heterogeneity and complexity Eubank et al Furthermore, searching for perfect models can be a distraction from the practical goals of a policy project Gilbert et al It is also important to remember the extreme time pressure the report was produced under.
Nevertheless, the particular presumptions encoded in Report 9 warrant interrogation. It provides no specificity regarding susceptibility beyond a correlation between age and hospitalisation. It does not account for how class, gender, race or ethnicity correlate with poorer health outcomes. The absence of race, ethnicity and class is particularly stark — considering not only data which emerged after the lockdown was put in place, but also information which was already available.
Report 10 was published on March 30th — four days after the social distancing measures were announced. Rather than a simulation, it used a more traditional epidemiological survey Atchison et al But recognition of such patterns of health inequality are largely absent from both Report 9 and government advice at the time Ferguson et al ; HM Cabinet Office Like Report 9, the report utilised an agent-based simulation. Although this model did not explicitly account for inequalities, it was made publicly available on publication and contextualised by data from sources other than simulations.
By relying on Report 9 and earlier models at the expense of these alternative sources of data, I argue the British government re-inscribed their featureless depictions of society in policy. In this positionality, knowers are not forced to consider discrimination, systemic racism or the ways these relate to them.
Through these absences, the report helped enact a necropolitics by omission — one that targeted systems and networks rather than people. Social simulations and the politics of knowledge. Behind this necropolitics lies a more general politics of knowledge. It is a politics of standpoints — of whose perspectives, knowledges, biases and concerns are centred in a model; of who has authority to access and interpret the results.
Complexity models have a complicated relationship with standpoint epistemology. On the one hand, CST implies that total access to information for any system is impossible and that knowers must themselves be situated within the wider system they wish to understand Cilliers It undermines claims to universal knowledge.
In turn, simulations which use CST are prone to being read as viewed as inherently objective — for example, when their data is used to generate an overview of a map, a network or a graph. I suggest this is evidenced in both the emergence of epidemiological simulations Eubank et al and more recent work in computational sociology such as Braha Simulations are constructed by people with culturally specific preconceptions and priorities.
In contrast, Report 9 was largely successful in staging its claims about Britain though not necessarily the US Ferguson et al Together, these formed a vastly complex assemblage of agents.
Hence, Report 9 could only have its effects with the correct citations, the proper institutional backing, successful coding, and a shared understanding of how to read its outputs. These networks can always break down. Aspects of its black box were under threat. Instead, it provided defence against challenges to the claims made using the simulation. The controversy was implicitly about where political responsibility lay.
Nor was the practice of pandemic modelling itself. Though media coverage may have skipped such nuance, the report itself is clear that it only provides a model based on explicit presumptions — not absolute truth Ferguson et al It does not point out the implicit and possibly unintentional judgements made regarding what factors were important in designing the simulations, or whose lives would be privileged by the recommendations it made.
Aside from their age-related susceptibility, each agent in the simulation is equivalent in its system. In turn, by black boxing the report, politicians and the press reframed its oversights as value neutral. Nuance may be lost in translation. Their predictions can appear like divinations, a kind of data ex machina.
As such, the priorities and perspectives reflected in Report 9 are those of a highly privileged minority relative to both the UK and the world.
British social simulation holds significant intellectual capital. Structured along neo colonial lines, theoretical output is dominated by well-endowed former metropolitan institutions like Imperial. Meanwhile, the former colonies are often used as sources of data before their academics become the recipients of models developed by their Global Northern colleagues.
This issue is particularly prevalent in social science Connell However, if British models were able to become international actors, it was not due to their intrinsic supremacy or greater objectivity. I contend these political dynamics apply for social simulations in general. They make possible the kinds of reifications and elisions present in Report 9.
Complexity models are particularly suitable to governmentalities which centre on management. Their forecasts and projections have helped provide reassurance in a rapidly changing environment where information is lacking. Hence, they have lent themselves to the intense uncertainty of the pandemic. But this might not have been the case. As such, a grounding in this risk-centred episteme was a precondition for the uptake of the simulations used to model the pandemic.
But as I discussed earlier, an ontology grounded in CST means that the nature of these risks can shift rapidly. Policymakers might be left reeling.
Report 9 changed the policy landscape again, as new data resulted in new emergent dynamics. It was able to do so due to the reliance on risk management which policymakers had developed.
Until that point, they displayed extraordinary resistance to challenging their epidemiological and ideological orthodoxies. Again, examining the ontological and epistemic implications of complexity modelling provides some insight into why: though it supports certain aspects of neoliberal governmentality, it severely undermines others.
Neoliberalism does not seem to have been destroyed, as some have hoped Lent ; Saad-Filho ; instead, it has been reassembled, with some tenets thrown out such as austerity and certain techniques reworked.
This underscores why neoliberalism is suited to being considered as Foucauldian governmentality Ong ; Ferguson and Gupta ; Li rather than simply an ideology for example, Harvey As a governmentality, neoliberalism characterises modalities of rule which centre on rolling back state power to allow for the flourishing of free markets and an entrepreneurial subject Foucault In practice, as many have noted, this requires an intensification of state regulation in many areas — particularly those deemed in non-economic, not productive enough or in need of marketising.
Ferguson and Gupta have pointed to how neoliberalism often does not mean increased autonomy. This is the governmentality against which neoliberalism was first developed. Epitomised by the Soviet Union, these planning projects were targeted by neoliberal theorists such as Friedrich Hayek. For Hayek, only the distributed decision-making of markets would allow for a free or just society — as centralised government knowledge will be inevitably limited in contrast to the total knowledge of economic actors Hayek In fact, Hayek later reframed this theory explicitly in terms of complex systems ibid.
Yet I suggest there is a crucial difference: the use of complexity models. Though simplified, these are designed to mitigate the issues Hayek and Scott critique. Their information problems are the result of non-linearity. Planners are seen as unaware of the risks caused by their limited knowledge or why their decisions will inevitably have harmfully unpredictable effects.
They are built to explore risks. As such, complexity modelling may provide states with the confidence to make grander interventions. It, and the models based on it, alter forms of rule even as they are used to support them.
It was significant not only as a case of extremely rapid policy reversal, nor even as a decision which affected the lives of millions. The aim was to keep markets open and place responsibility on individuals, avoiding state interference. This a very different form of bailout from the one given to major banks in Rather than individualist entrepreneurs, containing a contagion requires examining how people affect one another. Yet short-term interventionism should not be confused with a long-term move away from marketisation.
Indeed, the decisions made in weeks after March 16th show a continued delegation of state responsibilities to corporations. There is nothing in the relational, emergent, risk-centred premises of complexity theory which discounts the possibility of ever-more-flexible public-private synergies. Flexibility is its defining characteristic. It is more likely to involve new mixtures of marketisation and state intervention. Even as the old orthodoxies were broken, it retained the flexible technologies of exclusion and exception to the market which are characteristic of neoliberalism today.
Social simulations — with their ideological ambivalences — have been central to these changes. Whilst publics have become increasingly aware of and adapted to the ways these shape their lives, governments have been given a crash-course in their policymaking capabilities.
The UK may provide an extreme example, but it nevertheless points towards a trend. Social simulations have been used in policy for decades. Their underlying premises have shaped the risk-focused paradigms in which many governments have worked for over a generation. The crisis has pushed social simulations to the centre-stage of politics. As governments increasingly learn to lean on complexity models, work in computational social science may step in to fill their new need.
Again, the growth in scope and sophistication of computational social science — closely related to computational epidemiology — is not a new trend for examples, see the archives of the Journal for Artificial Societies and Social Simulation or work at the New England Complex Systems Institute.
But I argue that an acceleration may occur due to the confluence of new awareness resulting from the pandemic, new demands as new risks emerge in an increasingly unpredictable world, and ever improving capability could cause an acceleration.
This, in turn, stands a chance of generating more interest, funding and projects leading to more policy-driven implementation, contributing to the emergence of greater cultural awareness and contestation. In this scenario, the role of Report 9 would be a particularly dramatic picture of things to come. It would involve the intensification of many of the developments I have discussed.
These models must be seen as politically agentive in their own right, bringing their own kind of governmental politics. They are not merely constructs but actors whose natures have been altering the practices of governments for some time. This does not go to say that they have an essential power of their own.
Rather, they are empowered in specific situations. Epidemiological models, such as Report 9, must be primed for political action.
In the process, it played a key role in bio politics. In other words, there has been an intense politics over whether they are political. Doing so requires all the other necessary actors digital code, computer hardware, healthcare professionals, testing kits, civil servants, patients and, most of all, the virus — to fall in line. The stakes are incredibly high for COVID models: the victor is able to define which actors are responsible for the deaths of tens of thousands in the UK let alone the millions harmed across the planet.
The results of these knowledge controversies are intensely necropolitical. In fact, complexity models might help bring about the realities they model in a kind of feedback loop —triggering governments into carrying out the measures for which they have simulated the effects.
Unless they are presented explicitly alongside the data, any underlying presumptions or oversights in these premises are liable to being elided in the final results. When coded into policy, any oversights or biases could harm lives.
Moreover, if used unreflexively and as the only research method, simulations can become theoretically circular: the premises used to generate data cannot be challenged by that data within the closed environment of the simulation. Answers necessarily must be sought outside the model. Though unlike statistical or machine-learning models, social simulations work well with them to improve the accuracy of predictions. Moreover, the skewed, elite-favouring predominantly white, cis-male, middle-class political economies embedded into Big Datasets and machine learning techniques limits groups who can combine these methods: namely, influential institutions, rich corporations and powerful governments.
These benefits are equally accessible for liberal democracies and authoritarian regimes. These changes are indicative of the longer-term political effects of complexity science. In short, these are: non-linear behaviours, unpredictability, and limitations to centralised or universal knowledge undermining the idea of an all-seeing state. Yet whilst CST highlights the limitations to grand interventions, complexity simulations offer ways of mitigating the resulting risks.
Nevertheless, such mitigation requires adaptation to the epistemology and ontology CST prescribes. Neoliberalism is not immune here. Nothing in the theory explicitly undermines the potential for increasingly flexible and fluid public-private relationships. Policies based on complexity models, whether they target ecosystems or contagions, must reckon with this relationality somehow. Flexible, localised interventions are becoming a daily reality ibid. Meanwhile, epidemiological models continue to be central to these developments ibid; Clark ; Slawson However, beyond this immediate context, Report 9 illustrates how social simulations have a politics of their own.
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Newey, S. Nuki, P. Ong, A. Partington, R. Patel-Carstairs, S. Perrigo, B. Randhawa, G. Rathi, A. Rayner, H. Saad-Filho, A. Schaart, E. Scott, J. Seeing like a state: how certain schemes to improve the human condition have failed New Haven, Con. Shapin, S. Slawson, N. Spinney, L. Stewart, M. Sunak, R. Taleb, N. It isn't. Thatcher, M. Urry, J. Venturini, T. Wacquant, L. Walker, P. Ward, H. Weizman, E. Younge, G. Existing literature on the politics of climate change has been slow to respond to the resurgence of nationalism worldwide.
The free market's failure to internalise the cost of carbon emissions means that, due either to the effects of climate change or the rapidity at which we now have to cut emissions in order to meet our climate change targets, the future growth rate of the world economy is likely to be zero or less.
The article posits that this will exacerbate pre-existing nationalist competition arising from already-declining relative growth rates in the Global North when compared to countries like China, making such competition even more of a zero-sum game and thereby contributing to increasing international instability which fuels existential risks such as nuclear war.
In the free market economies of the Global North, declining growth rates also lead to financial crisis and inequality which aggravate the above-mentioned risks by fuelling nationalist sentiment amongst ordinary people.
The blind adherence to free market economics in the Global North has also led to a high rate of growth of migration, which is another key factor contributing to the rise of nationalist sentiment, and one that climate change will once again exacerbate. This article therefore contends that climate change and nationalism may come to interact with one another in a series of devastating feedback loops, with potentially catastrophic consequences.
He is now an MPhil student in Anthropocene Studies at Cambridge, where he aims to deepen his understanding of the political-economic relationship between climate change and nationalism, as explored in this article. Keywords: Climate change, nationalism, free market, growth, existential risk. However, this literature — and indeed the discipline of environmental political economy in general — has been slow to recognize the emerging reality, particularly visible within the past five years, of rising economic and political nationalism, which threatens to render the cosmopolitan and globally-minded solutions advocated by most environmentalist authors completely unviable.
In this essay I argue that both this resurgent nationalism and our most pressing environmental problem, climate change, have their common origins in particular limits to growth which are similar in nature. My argument is not so much that climate change and nationalism are in themselves existential risks , but rather that they act as drivers of international instability, which in turn severely increases existential risks such as nuclear conflict Centre for the Study of Existential Risk n.
My contention is that climate change and nationalism both arise from limits to the 'free' market. The free market's historic failure to internalise the cost of carbon emissions means that, due to the rapidity at which we now have to cut emissions in order to meet our climate change targets, the future growth rate of the world economy is likely to be zero or less.
I posit that this will exacerbate pre-existing nationalist competition arising from already-declining relative growth rates in the Global North when compared to countries like China, making such competition even more of a zero-sum game and thereby contributing to increasing international instability which fuels existential risks such as nuclear war.
In the free market economies of the Global North, declining growth rates lead to financial crisis and inequality which aggravate the above-mentioned risks by fuelling nationalist sentiment amongst ordinary people. I therefore contend that climate change and nationalism may come to interact with one another in a series of devastating feedback loops, with potentially catastrophic consequences. Economic means must instead be embedded in the broader social, cultural and physical environment. Climate change: limits to the market.
In the original Club of Rome report we can identify two conceptions of limits. A second category, however, involves limits relating to the unintended consequences of an activity, such as with pollution.
These limits present much more of a problem for defenders of the free market. The article identifies lessons learned concerning client populations and partnerships and suggests avenues for further study. Canada is poised to increase the number of migrants arriving annually. Growing attention is being directed toward how sport can be managed in a way that is accessible and inclusive of immigrant populations, as well as how sport can foster new opportunities for migrants to develop connections within their communities.
Youth sport programming is discussed as having little effect on the financial capacities and livelihoods of migrants.
As illustrated within this paper, sport has the ability to facilitate crosscultural relationships and influence acculturation strategies. However, sport-specific cultural capital produced asymmetries in the outcomes of sport participation. While sport may serve a role in developing social outcomes, efforts to improve the access of migrants to employment opportunities within their field of experience, either within or outside of sport contexts, are required to positively affect the livelihoods of migrants.
Three programs developed and delivered at MLSE LaunchPad, a large urban sport for development facility in Toronto, Canada, provide a precedent for further implementation and study of collaborative programs that incorporate intentionally designed sport activities into a youth employment program. Learnings to date at MLSE LaunchPad point to several key programming components for the successful delivery of youth sport for development employment training in a context of high youth unemployment rates disproportionately impacting youth facing barriers and a rapidly evolving job market.
Category archives for In Press Education is regarded as a human right and fundamental to achieving other human rights, such as decent work. Follow Following. Journal of Sport for Development Join 1, other followers.
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