I Get Knocked Down but I Get Up Again: Autonomous Motivation Sustains Identification and Collective Action After (Specific) Failure
Bibliographic record
Abstract
Movements often experience setbacks while striving to achieve (or prevent) social change. We examined whether autonomous motivation—which captures supporters’ internalized commitment to a cause—would sustain identification with the movement and collective action after experiencing failure (vs. success) outcomes following the marriage equality vote in Australia (Study 1; N = 186), and an experimental induction of movement failure (Study 2; N = 137). Autonomous motivation positively predicted identification and collective action, but there was no evidence of moderation by outcome. In Study 3 ( N = 377), we experimentally manipulated outcomes (success/failure) and framing (specific/broad) of the climate action movement. We found evidence of a three-way interaction such that the effects of autonomous motivation on identification were strongest after a specific campaign failure. We conclude that autonomous motivation can help to buffer the demotivating effects of a specific failure as well as sustaining identification and commitment to action broadly.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".