Bibliographic record
Abstract
In dual- process theories of recognition (Jacoby, 1991; Jacoby & Dallas, 1981; Mandler, 1980; Tulving, 1985), people have two bases for identifying a stimulus as previously encountered. Old judgements can arise from either a feeling of familiarity for the stimulus or from successful recollection of the context surrounding a prior exposure to that stimulus (see Yonelinas, 2002, for a review). This dual-process approach tends to orient researchers toward differences between the processes of familiarity and recollection. For example, feelings of familiarity are commonly seen as originating from a heuristic attribution process that is prone to error (Jacoby & Dallas, 1981; Jacoby & Whitehouse, 1989; Whittlesea, 1993; Whittlesea & Williams, 1998, 2000). In contrast, recollection is often seen as the more dominant and accurate basis for making recognition judgements. Indeed, rather than generated by a faulty heuristic attribution process, recollective experiences are seen as arising from a successful search of memory and a direct retrieval of details about the past into consciousness.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".