Conditional Cooperation in Public Goods Games and Perception of Corruption: A Comparative Study between Nigeria and Canada
Bibliographic record
Abstract
Conditional cooperation is the tendency to cooperate if and only if others cooperate. This paper aims to ascertain the importance of conditional cooperation and the effect of perceived corruption and cultural environments on individuals’ behaviors in the public goods games in a public game by replicating the seminal Fischbacher et al. (2001) experiment and comparing results between Nigeria and Canada. This thesis proposes to explore the reliability of classifying participants into distinct behavioral types – such as conditional cooperators, free riders, and triangular contributors – based on their contribution patterns. Additionally, a post-experiment questionnaire – which is modified based on the European Social Survey (ESS), Value Survey Model (VSM, 2013), and the United Nations Office on Drugs and Crime (UNODC) and National Bureau of Statistics (NBS) National Survey on Quality and Integrity of Public Services – is employed to explore how individuals in both countries perceive their cultural and socio-economic differences, and how perceptions, particularly regarding corruption, influence their cooperative behavior and contributions to public goods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".