An updated meta-analysis of the primed goal-organizational behaviour relationship
Bibliographic record
Abstract
Environmental cues (e.g. achievement-related words and pictures) can prime/activate, in the absence of awareness, a mental representation of importance stored in memory. Chen et al .'s 2021 Applied Psychology: An International Review 70 , 216–253. ( doi:10.1111/apps.12239 ) meta-analysis revealed a moderate, significant overall effect for the goal priming-organizational behaviour relationship, with three moderators identified: context-specific versus a general prime, prime modality (i.e. visual versus linguistic) and experimental setting (field versus laboratory). An independent researcher found that their finding was negligibly affected by a publication bias. Shanks & Vadillo (2021), Royal Society Open Science 8 , 210544. ( doi:10.1098/rsos.210544 ) (field: k = 13, N = 683, d = 0.64), questioned Chen et al .'s conclusion regarding the effect size found in field studies (field: k = 8, N = 357, d = 0.68). In this paper, we discussed Shanks & Vadillo's selection of additional field experiments that led to their conclusion of a publication bias. We updated Chen et al .'s meta-analysis to include relevant studies conducted since that study's publication. The present meta-analysis reproduced the original findings in Chen et al . (field: k = 11, N = 534, d = 0.67). The updated findings are consistent with: (i) laboratory findings, (ii) the findings obtained in field experiments on consciously set goals and (iii) goal setting theory (Latham & Locke, 2018 In Handbook of industrial, work & organizational Psychology , vol. 1 (eds D Ones, N Anderson, C Viswesvaran, H Sinangil), pp. 103–124).
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.035 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".