Expert-Novice Differences in Memory: A Reformulation
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
BACKGROUND: One of the most discriminating measures of expertise in multiple domains has been performance on memory tasks. In medicine, however, the relation between expertise and memory is more equivocal. PURPOSE: To compare and contrast the sufficiency of multiple explanations of this finding by using three probes of memory rather than the traditional free recall task alone. METHODS: Students, residents, and internists were asked to read case histories and assign diagnoses before undertaking free recall, cued recall, and recognition tests. RESULTS: Students consistently outperformed internists. Resident performance was more variable. CONCLUSIONS: Our data appear to rule out (a) the notion that expert memory for cases takes on an encapsulated form, (b) the idea that experts simply say less than students in response to a free recall task, and (c) the possibility that experts attend differentially to highly diagnostic features. The results can best be explained by the idea that students process the featural details of a case history more elaborately than do expert diagnosticians who, instead, read medical cases more holistically.
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 | 0.002 | 0.047 |
| 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 | 0.001 |
| 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