Generalization versus contextualization in automatic evaluation.
Bibliographic record
Abstract
Research has shown that automatic evaluations can be highly robust and difficult to change, highly malleable and easy to change, and highly context dependent. We tested a representational account of these disparate findings, which specifies the conditions under which automatic evaluations reflect (a) initially acquired information, (b) subsequently acquired, counterattitudinal information, or (c) a mixture of both. The account postulates that attention to contextual cues during the encoding of evaluative information determines whether this information is stored in a context-free representation or a contextualized representation. To the extent that attention to context cues is low during the encoding of initial information but is enhanced by exposure to expectancy-violating counterattitudinal information, initial experiences are stored in context-free representations, whereas counterattitudinal experiences are stored in contextualized representations. Hence, automatic evaluations tend to reflect the valence of counterattitudinal information only in the context in which this information was learned (occasion setting) and the valence of initial experiences in any other context (renewal effect). Four experiments confirmed these predictions, additionally showing that (a) the impact of initial experiences was reduced for automatic evaluations in novel contexts when context salience during the encoding of initial information was enhanced, (b) context effects were eliminated altogether when context salience during the encoding of counterattitudinal information was reduced, and (c) enhanced context salience during the encoding of counterattitudinal information produced context-dependent automatic evaluations even when there was no contingency between valence and contextual cues. Implications for automatic evaluation, learning theory, and interventions in applied settings are discussed.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".