Negative Effects of Reward on Intrinsic Motivation—A Limited Phenomenon: Comment on Deci, Koestner, and Ryan (2001)
Bibliographic record
Abstract
A major concern in educational settings is that the use of rewards and incentives may destroy students’ intrinsic motivation to perform activities. In collaboration with other researchers, the author conducted a meta-analysis of the literature that showed that negative effects of reward were limited and easily avoidable (Cameron & Pierce, 1994; Eisenberger & Cameron, 1996 ). Deci, Koestner, and Ryan (2001) suggest that our work was seriously flawed; they present a summary of their meta-analysis on the topic (Deci, Koestner, & Ryan, 1999a) and claim that rewards do substantially undermine intrinsic interest. In this comment, it is argued that there is no inherent negative property of reward. By organizing studies according to cognitive evaluation theory, Deci et al. (1999a) collapsed across distinct reward procedures and were able to obtain pervasive negative effects. When studies are organized according to the actual procedures used, however, negative effects are limited to a specific set of circumstances.
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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 | 0.040 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".