Bibliographic record
Abstract
We all have models of the world, and when these models fit with what goes on around us we have a sense of meaning. Unfortunately, we are often faced with situations that violate, or threaten, our models, and when this happens we attempt to resolve these inconsistencies to restore a sense of meaning. It is well documented that we often try to reduce threats in indirect ways-ways that, at first glance, seem to reduce the negative feelings without actually solving the problem. This article explores the possibility that threats can be interpreted in different ways depending on the person and context, and suggests that because of this, different threat reduction approaches can be adaptive in different situations. Specifically, it presents the hypothesis that concrete construal of threats should result in compensation efforts that are relatively direct, whereas abstract construals should expand the possibilities for compensation to include indirect strategies. It describes the existing evidence, where evidence is lacking, and potentially fruitful avenues of future exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.071 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.013 |
| 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".