Bibliographic record
Abstract
This paper considers how science fiction, and the subgenres of speculative historicism and futurism in particular, might open legal discourse to hitherto unseen and potentially instructive perspectives. It begins with the proposition that recent historical events of global significance such as the election of Donald Trump in 2016, the outbreak of the Covid19 pandemic of 2020, and the extreme weather events of 2021, were widely predicted and foreseen in the media by way of political reporting as much as popular social and natural science reporting in the years and decades prior. The same tropes were also present in the plotlines of popular literature, television, and film during that period. The central argument of the paper is that before media pundits and policy-makers expressed their surprise at the fragility of the Rule of Law in the “unprecedented” ascent of Trump, the lethal capacity and transmissibility of a “novel” coronavirus, and the “sudden” arrival of climate change in the daily lives of North Americans and Europeans, the spectre of these menaces had already penetrated our collective conscious in a way that ought to have changed outcomes. Neil Postman’s conceptualization of the present epoch as “Technopoly” is a means of explaining how, despite ample warnings, we were not ready for much. Technopoly refers to the historical present as the historical moment in which the technocratic capacity of individuals, states, and markets to respond to existential problems is hindered by information overload, e.g., the threat to the Rule of Law presented by an outgoing American President who refuses to accept the verdict of the electorate; the threat to public health posed by persistent vaccine misinformation and inequitable global vaccine distribution; and, the threat posed to our collective habitat by extreme climate events. The paper concludes that fiction is a powerful potential antidote to the numbing effects of information overload in Technopoly if it is treated seriously as a source of normative authority rather than dismissed as pure diversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".