Bibliographic record
Abstract
Seen through the lens of Republican candidate Donald Trump’s reality TV program ‘The Apprentice,’ his promise to American voters that they’ll tire of “winning” under his regime takes on a darker meaning. This article identifies ‘the loser’ as a potent new political symbol emblematic of ‘contestants’ who in the face of mathematical loss become ‘bigger’ losers if they fail to assert their right to a non-meritorious victory. The fact of one’s loss is not as important as one’s reaction to it. To lose is possible, but to be a ‘loser’ is the ultimate humiliation that justifies taking extreme, even immoral measures. Contestants who are willing to ‘do anything’ to win are rewarded more generously often than those who, in reality, are the rightful winners. Such a perspective rationalizes a politics of exaggerations, lies and defamation. Extending Couldry and Littler’s discourse of passion, we identify the mechanism that enables and compels some voters to embrace Trump’s divisive politics of ‘otherism’ as astute ‘game playing.’ In Trump’s world, to win means many more must lose. Just as in the reality TV world, however, Trump alone holds the power to annoint winners and exile losers, meaning there is no guarantee of success for anyone but him.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".