It Works in Practice: Does It Work in Theory?
Bibliographic record
Abstract
"In 1968, Hardin argued that all commonly-owned resources would tragically be depleted unless private ownership was granted. There are many case studies which prove Hardin wrong. Common-pool resources have been managed with success. However, this success does not imply (as some believe) that communal ownership and management 'works' and is the appropriate management style for all resources. At the very least, the word 'works' needs definition. \n \n"The Japanese village of Hirano used a lottery mechanism to distribute winter fodder gathered on village-owned land. It is true that the fodder gathering and distribution system worked- the villagers used this system from the 1600's to the 1950's. But, the question remains: was the mechanism effective in curtailing excessive harvesting from the commons? The results of this economic experiment suggest that the lottery mechanism greatly enhances the efficient use of the resource by reducing individual incentives to over-appropriate. Despite the effectiveness of this mechanism, it is not the case that individuals act in the manner suggested by economic theory. Further research is necessary to understand how individuals operate in this environment."
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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".