The Effects of Volunteerism on Self-Deception and Locus of Control
Bibliographic record
Abstract
Abstract This study examines whether volunteering for not-for-profit Organizations (NPOs) which are involved in providing social welfare services and which actively promote sociobehavioral factors like social responsibility, leadership, and self-confidence among its volunteers, reduces an individual’s likelihood of engaging in corrupt practices. We identify two psychological traits: propensity to rationalize (as evidenced by self-deception) and an external locus of control (as compared to an internal LOC) that facilitate unethical behavior. With the help of volunteers from two NPOs, we investigate whether engaging in social welfare activities organized by such NPOs would create awareness about the adverse consequences of corruption faced by large segments of the society, which in turn would make it difficult to rationalize unethical and corrupt acts. Additionally, most NPOs actively strive to develop self-confidence and leadership skills among its volunteers. Prior literature indicates that individuals possessing such qualities are more likely to have an internal LOC and also that individuals possessing an internal LOC are less likely to act in a corrupt manner. The overall results indicate that greater experience with such NPOs leads to a significant reduction in propensity to rationalize and leads to a higher likelihood of having an internal LOC.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".