Cooperation and framing effects in provision point mechanisms: Experimental evidence
Bibliographic record
Abstract
Andreoni (1995) showed that pure framing effects may influence contribution in Voluntary Contribution Mechanisms (VCM) by comparing a standard public goods game, called the positive frame condition (giving to the public good), with a negative frame condition (taking from the public good) where the subjects' choice to purchase a private good makes the other subjects worse off. This paper aims at testing the robustness of such framing effects in the context of Provision Point Mechanisms (PPM). Our approach is original in that it combines both framing and provision point dimensions by comparing maintaining (taking from the public good) and creating (giving to the public good) contexts using Provision Point experiments. Consistent with previous findings, we find that individuals tend to be less cooperative in the maintaining frame than in the creating frame. Our results also show that the framing effects are stronger under a PPM than under a VCM and increase with the provision point level. These results may have important consequences for the management of environmental resources.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".