Rewards and Intrinsic Motivation: Resolving the Controversy
Bibliographic record
Abstract
Introduction An Introduction to the Rewards and Intrinsic Motivation Controversy Rewards and Intrinsic Motivation: A Look at the Early Studies How Rewards Got a Bad Reputation Why Rewards Don't Deserve a Bad Reputation Theoretical Disputes Over Rewards and Intrinsic Motivation Theoretical Perspectives of Rewards as Harmful Theoretical Perspectives of Rewards as Helpful The Empirical Evidence for the Impact of Rewards on Intrinsic Motivation An Overview of Rewards and Intrinsic Motivation Experiments A Critique of Meta-Analysis on the Effects of Rewards on Intrinsic Motivation A Meta-Analyses of the Effects of Rewards on Intrinsic Motivation Discussion and Implications of the Meta-Analytic Findings Rewards and Intrinsic Motivation: A Socio-Historical Perspective A Socio-Historical Analysis of the Rewards and Intrinsic Motivation Literature Practical Applications of Rewards The Effective Use of Rewards in Everyday Life Conclusion Resolving the Controversy Over Rewards and Intrinsic Motivation References Index
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 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".