Generalization versus contextualization in automatic evaluation revisited: A meta-analysis of successful and failed replications.
Bibliographic record
Abstract
To account for disparate findings in the literature on automatic evaluation, Gawronski, Rydell, Vervliet, and De Houwer (2010) proposed a representational theory that specifies the contextual conditions under which automatic evaluations reflect initially acquired attitudinal information or subsequently acquired counterattitudinal information. The theory predicts that automatic evaluations should reflect the valence of expectancy-violating counterattitudinal information only in the context in which this information had been learned. In contrast, automatic evaluations should reflect the valence of initial attitudinal information in any other context, be it the context in which the initial attitudinal information had been acquired (ABA renewal) or a novel context in which the target object had not been encountered before (ABC renewal). The current article presents a meta-analysis of all published and unpublished studies from the authors' research groups regardless of whether they produced the predicted pattern of results. Results revealed average effect sizes of d = 0.249 for ABA renewal (30 studies, N = 3,142) and d = 0.174 for ABC renewal (27 studies, N = 2,930), both of which were significantly different from zero. Effect sizes were moderated by attention to context during learning, order of positive and negative information, context-valence contingencies during learning, and sample country. Although some of the obtained moderator effects are consistent with the representational theory, others require theoretical refinements and future research to gain deeper insights into the mechanisms underlying contextual renewal.
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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 | 0.119 | 0.235 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.026 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".