Effects of Construal Level on Omission Detection and Multiattribute Evaluation
Bibliographic record
Abstract
ABSTRACT Research has demonstrated that consumers are commonly insensitive to missing information and that this insensitivity can lead them to form strong beliefs and evaluations on the basis of weak evidence. A growing body of research has shown that sensitivity to omissions can be heightened and that this increased sensitivity results in more appropriate evaluations. Expanding on this, the current research finds that the level of abstraction by which a situation is construed can influence the likelihood of omission detection and the resulting evaluative judgments. A series of studies reveal that people are more likely to spontaneously detect omissions in near vs. distant judgments, in concrete vs. abstract mindsets, and when they are inherently more likely to interpret actions in concrete vs. abstract terms. Further, although prior findings suggest that people may have differential sensitivity to primary and secondary missing features at different levels of construal, the current research finds no such difference. The results of this study indicate that people are more sensitive to all types of missing information when construal levels are low, and that this sensitivity leads to more moderate and appropriate judgments.
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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 | 0.012 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".