Two sources of information in reconstructing event sequence.
Bibliographic record
Abstract
Reconstructing memory for sequences is a complex process, likely involving multiple sources of information. In 3 experiments, we examined the source(s) of information that might underlie the ability to accurately place an event within a temporal context. The task was to estimate, after studying each list, the temporal position of a single test word within that list. In the first 2 experiments, we demonstrated that memory for temporal location was better following semantic encoding than silent reading of the list, which in turn was better than orthographic encoding of the list. Although other measures of sequence retention have revealed impaired memory for order with greater item-level encoding, these experiments demonstrated that item-level encoding improved memory for temporal-location. A 3rd experiment extended these findings by measuring interitem associations in addition to item memory, demonstrating that memory for temporal location within a list was more closely related to item information than to interitem relational information. It is now clear that reconstructing an event sequence can involve at least 2 distinct sources of information-both item and relational encoding can play important roles, depending on the nature of the test for order. (PsycINFO Database Record
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".