Noticing and Revising Discrepancies as Texts Unfold
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
Readers attempt to build coherent representations for what they read, but those representations may fail to capture the actual content of texts. For example, although narrative situations often change dramatically as plots unfold, readers do not necessarily revise what they know to accurately represent the current state of affairs in a text. This study investigated the conditions that might foster revision, and the temporal locus of potential revision activity. In 2 experiments, participants read stories that afforded the opportunity to build trait models of characters. Trait descriptions were either immediately refuted or supported with further evidence. Participants revised their models of characters when provided with causal explanations. They did not revise, however, when previous character information was simply refuted. Revision, when it occurred, was observed immediately after refutations were provided. Whether they revised or not, though, participants appeared to readily notice the discrepancies suggested by refutations. The results of this study further outline the nature of narrative updating, as well as the revision failures that can influence readers' comprehension of unfolding texts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it