Representing and remembering text paraphrases: a phantom recollection analysis
Bibliographic record
Abstract
This study was designed to evaluate the memory representations that support complex patterns of readers’ memorial judgments about text paraphrases. This issue was examined with reference to the phantom recollection model. That analysis considers memory judgments to be collaboratively supported by one’s recollection of an item in its context, a vaguer sense of stimulus familiarity, and the phantom recollection of the substance and even perceptual details of unstudied but related lures. In two experiments, subjects read blocks of brief passages and then judged explicit, paraphrased, control (lure) test items, and inference statements. Different subject groups were instructed to base their judgments (a) on a verbatim or “recognize” criterion, (b) on a gist criterion, or (c) to accept only items implied but not stated in their passages. Multinomial tree processing analysis was applied to the data. Both paraphrases and coherence-preserving (“bridging”) inferences were supported by phantom recollection. However, familiarity was greater for the former, reflecting the greater overlap of paraphrases than inferences with the perceptual details of the text. Some minor deviations between these results and those of prior studies are addressed. The joint application of the empirical paradigm and multinomial tree processing exposes the representations that support readers’ retrieval from text.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".