Many Labs 5: Registered Replication of Albarracín et al. (2008), Experiment 5
Bibliographic record
Abstract
In Experiment 5 of Albarracín et al. (2008), participants primed with words associated with action performed better on a subsequent cognitive task than did participants primed with words associated with inaction. A direct replication attempt by Frank, Kim, and Lee (2016) as part of the Reproducibility Project: Psychology (RP:P) failed to find evidence for this effect. In this article, we discuss several potential explanations for these discrepant findings: the source of participants (Amazon’s Mechanical Turk vs. traditional undergraduate-student pool), the setting of participation (online vs. in lab), and the possible moderating role of affect. We tested Albarracín et al.’s original hypothesis in two new samples: For the first sample, we followed the protocol developed by Frank et al. and recruited participants via Amazon’s Mechanical Turk ( n = 580). For the second sample, we used a revised protocol incorporating feedback from the original authors and recruited participants from eight universities ( n = 884). We did not detect moderation by protocol; patterns in the revised protocol resembled those in our implementation of the RP:P protocol, but the estimate of the focal effect size was smaller than that found originally by Albarracín et al. and larger than that found in Frank et al.’s replication attempt. We discuss these findings and possible explanations.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".