Bibliographic record
Abstract
To explore the strategy use in associative recognition, we constructed two word-triplet lists to represent the information networks in the real world featured by repetition, co-occurrence, and change. We predicted that word-triplet recognition would depend upon the co-occurrence of repeated context words and nonrepeated unique words within a list, and the word change between two lists. In Experiment 1, we compared the probability of accepting the triplet test trials that consisted of: (a) different numbers of word links between context words and unique words, and (b) context words from same or different lists, and we found that recognition judgments only relied on the retrieval of word links. In the follow-up experiments, we increased participants' awareness of list-membership cues by explicitly informing them of the word change between lists prior to triplet encoding (Experiment 2), and by using self-generated context words from two lifetime periods (Experiment 3). The results suggested that participants might use a strategy based on both the retrieval of word links and list-membership cues, but only if they perceived the between-list word change during encoding. The present research provides new evidence for Transition Theory using the approach of word-triplet recognition. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.016 |
| 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 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".