Bibliographic record
Abstract
This paper examines the critical potential and pedagogic possibilities of the workplace television comedy during the COVID-19 pandemic. It is particularly interested in Greg Daniels’ Upload, a series that debuted during the first wave of infections in North America. Although it was produced before the current health crisis, Upload offers a prescient social commentary on the depravities of late capitalism, one that speaks to present concerns over access to vital health resources and the importance of “essential” workers, specifically in the service industry. However, Upload is driven by a liberal version of multiculturalism that emphasizes racial equality and ostensibly recognizes “difference” but downplays economic disparities and racial divisions of labor even as it draws on them repeatedly in its critique of inequality. Whereas the series effectively challenges the monetization of everyday life and death, a point of praise for many critics, it fails to disrupt or even call attention to how low-wage and purportedly low-skilled work is currently and historically racialized in the US. As a result, Upload’s efficacy as a social commentary and indictment of late capitalism is more interested in the unequal distribution of essential resources and services than the political economy of their provision, that is, the social relations of their production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".