Bibliographic record
Abstract
Why do some people trust their gut instincts over logic? It could be that they see those snap decisions as a more accurate reflection of their true selves and therefore are more likely to hold them with conviction, according to research published in Emotion, a journal of the American Psychological Association (APA). “Focusing on feelings as opposed to logic in the decision‐making process led participants to hold more certain attitudes toward and advocate more strongly for their choices,” lead researcher Sam Maglio, Ph.D., an associate professor of marketing at the University of Toronto Scarborough, said in an APA press release. The series of four experiments involved more than 450 participants, including local residents, undergraduate students and online survey‐takers. In each experiment, participants had to choose from a selection of similar items, such as different DVD players, mugs, apartments or restaurants. Participants were asked to make their decision either in a deliberative, logical manner or in an intuitive, gut‐based one. Participants who were instructed to make an intuitive, gut‐based decision were more likely to report that that decision reflected their true selves. “Our research suggests that individuals focusing on their feelings in decision‐making do indeed come to see their chosen options as more consistent with what is essential, true and unwavering about themselves,” said Maglio.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.142 | 0.070 |
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".