Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again
Bibliographic record
Abstract
The finding that extrinsic rewards can undermine intrinsic motivation has been highly controversial since it first appeared ( Deci, 1971 ). A meta-analysis published in this journal (Cameron & Pierce, 1994) concluded that the undermining effect was minimal and largely inconsequential for educational policy. However, a more recent meta-analysis (Deci, Koestner, & Ryan, 1999 ) showed that the Cameron and Pierce meta-analysis was seriously flawed and that its conclusions were incorrect. This article briefly reviews the results of the more recent meta-analysis, which showed that tangible rewards do indeed have a substantial undermining effect. The meta-analysis provided strong support for cognitive evaluation theory (Deci & Ryan, 1980), which Cameron and Pierce had advocated abandoning. The results are briefly discussed in terms of their relevance for educational practice.
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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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| 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".