Bibliographic record
Abstract
Abstract This paper discusses the inefficacy problem that arises in contexts of “collective harm.” These are contexts in which by acting in a certain sort of way, people collectively cause harm, or fail to prevent it, but no individual act of the relevant sort seems to itself make a difference. The inefficacy problem is that if acting in the relevant way won't make a difference, it's unclear why it would be wrong. Each individual can argue, “things will be just as bad whether or not I act in this way, so there's no point in doing otherwise.” The goal of this paper is to give an overview of some of the main responses available to the problem and to highlight central issues that arise for each type of response. In the final section, I explain what I take to be the most promising strategy and discuss the form that this strategy should take.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".