Bibliographic record
Abstract
People are motivated to maintain the belief that they live in an orderly world in which things are under control. Previous research has shown that perceptions of order can be maintained via two routes: affirming personal control over one’s life and future outcomes, and bolstering one’s belief in external systems or agents that exert control over the world. Both religion and sociopolitical institutions can provide subjective and socially sanctioned security in the context of low personal control or disorder in one’s environment. In this article, we argue that belief in science and progress could serve a similar function. Science is not only assumed to simplify people’s lives; it also creates a sense of order and predictability. We show that perceiving order (regardless of external agency) can be sufficient to combat lack of control, and that perceptions of order can be derived from science and from more general beliefs about progress. We also discuss findings from our research addressing the processes underlying these effects and the functionality of compensatory beliefs and perceptions. We conclude that endorsing scientific theories and beliefs in societal and scientific progress helps people regulate threats to order and control, as long as these theories and beliefs suggest that the world is (or will be) an orderly place.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.380 | 0.392 |
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".