Bibliographic record
Abstract
Abstract Objective: To use meta-analysis techniques to assess the impact of various factors on the extent of cooperation in standard linear public goods experiments using the voluntary contributions mechanism. Data Sources: Potentially relevant experiments were identified through searches of EconLit, the Internet Documents in Economics Access Service (IDEAS), and a survey article. Review Methods: A total of 349 potentially relevant studies were identified. Of these, 27 (representing a total of 711 groups of participants) met the inclusion criteria. Data were abstracted from these studies using a standardized protocol. Results were analyzed using weighted ordinary least squares. Average group efficiency was the dependent variable. Results: The marginal per capita return, communication, constant group composition over the session (“partners”), positive framing, and the use of children as subjects had a positive and significant effect (p < 0.05) on the average level of contribution to the public good. Heterogeneous endowments to subjects, experienced participants, and soliciting subjects’ beliefs regarding other participants’ behaviour prior to the start of the session/period had a negative and significant effect. A number of other factors were not identified as significant. Conclusion: The meta-analysis results parallel several key findings from previous literature reviews. In addition, they offer parameter estimates and an analysis of significance based on the totality of the available research evidence. More consistent reporting of the results of experiments would greatly improve the ability to conduct this type of research.
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 | 0.108 | 0.211 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".