Bibliographic record
Abstract
Nothing facilitates large-scale collaboration like the prospect of inclusive, all-win games. Modern humans have gotten much better at large-scale collaboration because they have discovered, or invented, a broad range of collective goods that are easy to share and become more valuable the more they are shared, thus multiplying the opportunities for all-win outcomes. Steven Weber (2004) and Mark Cooper (2006a, 2006b) have drawn our attention to ‘antirival goods’ — subject to increasing returns to shared use — to differentiate them from ‘rival goods’ — subject to decreasing returns to shared use — and ‘nonrival goods’ — subject to constant returns to shared use. Unlike Weber and Cooper, I argue that nonrivalness and antirivalness are orthogonal properties of some collective goods, rather than stages along the same continuum away from rivalness. Collective goods, I also argue, are most inclusive when they are both nonrival and antirival. In an economy rich in both nonrival and antirival goods, the collaborative stance will often be the default collective choice, at large and small scales alike. Digital technologies are ushering in a transformative age as they expand the cornucopia of nonrival and antirival goods available to us. This inclusiveness of many digital goods eliminates the free-riding problem and mobilizes large amounts of volunteer work.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".