Bibliographic record
Abstract
Previous work suggests that similar cognitive processes contribute to memory and comprehension. This is unsurprising as both begin with a common process: encoding. Despite this, the investigation of techniques that benefit memory and comprehension has proceeded separately. In the current study, we compared the robust memory techniques of production and drawing to a similarly effective comprehension strategy known as paraphrasing. Depending on the group, participants were asked to either engage in one of the encoding types (read aloud, draw, or paraphrase) or to silently read 20 term-definition pairs (randomly intermixed and counterbalanced). The encoding techniques of drawing and paraphrasing resulted in better performance on a multiple-choice test of concept comprehension, relative to silently reading. By contrast, reading aloud at encoding did not lead to any benefit relative to silently reading. The results suggest that techniques that invoke transformation of the to-be-remembered text into another format, be it into a picture (drawing) or personally relevant summary (paraphrasing), are particularly effective at improving comprehension. By contrast, encoding techniques that mainly provide a perceptual repetition (production and silent reading) are less effective.
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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".