Defeating the Potentially Deleterious Effects of Externally Imposed Deadlines: Practitioners’ Rules-of-Thumb
Bibliographic record
Abstract
The authors interviewed people to determine whether they devise strategies to offset the damaging effect that externally imposed deadlines have on intrinsic motivation. Interviewees' "practitioners' rules-of-thumb" strategies were consistent with the tenets of self-determination theory and were tested empirically in three experiments. In each of the experiments, complete or partial self-determination of initially externally imposed time limits negated the otherwise deleterious effects of deadlines on intrinsic motivation. Participants who actively co-opted a deadline as their own (Experiment 1), who self-imposed sub deadlines within an overall externally imposed deadline (Experiment 2), and who self-imposed more stringent deadlines than those imposed externally (Experiment 3) spent significantly more free-choice time engaged in target tasks than did their counterparts in externally imposed deadline conditions where no self-determination was permitted. Given the ubiquity of deadlines, the results can directly be implemented by both deadline setters and deadline recipients to protect people's interest in their work.
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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 | 0.022 | 0.090 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".