Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research across a variety of domains has found that people fail to evaluate statistical information in an atheoretical manner. Rather, people tend to evaluate statistical information in light of their pre-existing beliefs and experiences. The locus of these biases continues to be hotly debated. In two experiments we evaluate the degree to which reasoning when relevant beliefs are readily accessible (i.e., when reasoning with Belief-Laden content) versus when relevant beliefs are not available (i.e., when reasoning with Non-Belief-Laden content) differentially demands attentional resources. In Experiment 1 we found that reasoning with scenarios that contained Belief-Laden content required fewer attentional resources than reasoning with scenarios that contained Non-Belief-Laden content, as evidenced by smaller costs on a secondary memory load task for the former than the latter. This trend was reversed in Experiment 2 when participants were instructed to ignore their beliefs when reasoning with Belief-Laden and Non-Belief-Laden scenarios. These findings provide evidence that beliefs automatically influence reasoning, and attempting to ignore them comes with an attentional cost.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it