Bibliographic record
Abstract
Many social institutions function with rivalry, whether it is the legal adversarial system, the electoral system, competitive sports or the market. The literature on adversarial ethics (with authors such as Arthur Applbaum, David Luban and Joseph Heath) attempts to clarify what is a good behavior in these situations, but this work does not examine if institutionalized rivalry is desirable given its good and bad aspects. According to Monroe Freedman, for instance, the confrontation between lawyers in a trial may help discover important facts about a case. Most economists believe that competition in the market increases economic efficiency. But institutionalized rivalry can also lead to morally wrong acts such as violence, deception or coercion. The aim of this article is to identify the conditions under which rivalry may be more or less desirable in our social arrangements. First, it will be necessary to clarify what is institutionalized rivalry and what is an adversarial scheme. Then, this article will explain what are the generic advantages and problems of adversarial schemes. Finally, this analysis will be used to outline a series of minimal requirements to consider that an adversarial scheme is desirable.
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 | 0.045 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".