Collaborative Registered Replication of Griskevicius et al. (2010): Can Pro-environmental Behavior Be Promoted by Priming Status Motivation?
Bibliographic record
Abstract
The present study presents the results of a collaborative registered replication of Griskevicius et al. (2010, Experiment 1). As part of the Collaborative Replication and Education Project, 24 student groups from six countries (N = 3,774) investigated whether pro-environmental behavior can be promoted by priming status motives (desires for social status and prestige). This large, multi-site replication showed no evidence to support the hypothesis that hypothetical pro-environmental behavior can be stimulated by having participants read a story designed to prime status motives. We performed several exploratory analyses to investigate whether extension variables (i.e., equating “green” choices with prosocial behavior, political beliefs, sampling methods, location, duration of data collection, and gender) moderated the hypothesized effect of status motives on pro-environmental choices, but these analyses produced null results. One limitation of the study is that most data collection sites did not include a manipulation check, and the one site that did found a much weaker effect (d = 0.32) than the extremely large effect originally reported (d = 3.69). As a result, it remains unclear whether the null result reflects a failure of this specific priming method or a challenge to the underlying theory.
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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 | 0.025 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".