Investigating the Effects of Computer-Generated Contextual Landmarks on Short-Term Recall of E-Texts
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
E-texts have many advantages over their paper counterparts, especially when they are reflowable and available as open educational resources (OERs). Unfortunately, research suggests that e-texts are, on the whole, less memorable than p-texts, in part due to their relative lack of visual navigational landmarks that help to anchor recall. The Landmarks project team is, therefore, building an application that inserts computer-generated artificial imperfections – abstract or representational landmarks – into the display of e-texts, that remain consistently associated with text passages even when documents are reflowed or reformatted. We hypothesize that it may consequently be easier to recall the associated contents. The application is designed to provide the means to present modified open texts using a range of generated landmarks and variations on them, and to test recall of the content. In this initial pilot study, results of tests for readers receiving different landmarks will be compared, with the intent of identifying promising approaches to use for future studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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