Recall Salience: Concept, Use, and Estimation
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
A 1998 review of the 1994, 1996, and 1997 Canadian Travel Surveys (CTS) provided evidence that a large decline in estimated travel was related to a change in respondents' efficiency in recalling trips, in other words, to a change in trip recall salience (TRS) between the years. Because the CTS data are collected on all trips that respondents take in a month, one can examine the order in which different categories of trips are reported. Research reviewed in this article shows how the statistical significance of TRS and the estimation of a TRS scale can occur. Scale estimation is critical to work cited as making estimates of the consequence of changes in survey methodology. This research pursues the systematic estimation of TRS scales using regression. Topics covered include avoiding bias, estimation of a TRS scale using regression, and estimating bias in the CTS using a TRS scale. Because numerous surveys collect data on occurrences recalled for a given period of time, it follows that some analyses where salience is relevant will be based on data sets large enough that the ideas presented and the methodology developed will be of benefit. For other studies a caution is discussed about the impact of salience, even if a scale cannot be estimated and recall bias evaluated.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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