Recall Salience: Concept, Use, and Estimation
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.
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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 | 0.052 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".