Bibliographic record
Abstract
There are many sorts of day-to-day choices that are such that, if enough people were to choose one way rather than another, serious harm could be avoided or reduced, and yet it does not seem that any one such choice will itself make a difference. Consider, for example, how our collective consumer choices have various serious environmental and social consequences, and yet for many products, it is doubtful that one purchase more or less will itself make a difference to these outcomes. How are we to understand what each of us ought to do in these sorts of contexts? This paper further advances and illuminates a thesis that I have argued for elsewhere: that a purely ‘non-instrumental’ approach to this question is not satisfactory. A necessary and central part of understanding how to think about an individual choice in these contexts is showing that it does matter for instrumental reasons - for reasons having to do with its ability to have an influence on the outcome. Once a core instrumental solution is found, other moral considerations can build on top. I argue for this by way of an examination of a new non-instrumental approach advanced by Wieland and van Oeveren: a participation-based approach. I also identify what I think is the main source of resistance to my thesis: a mistaken conflation of two different problems.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.079 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".