Age differences in trust and reliance of a medication management system
Bibliographic record
Abstract
Journal Article Age differences in trust and reliance of a medication management system Get access Geoffrey Ho, Geoffrey Ho Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 Search for other works by this author on: Oxford Academic Google Scholar Dana Wheatley, Dana Wheatley Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 Search for other works by this author on: Oxford Academic Google Scholar Charles T. Scialfa Charles T. Scialfa * Department of Psychology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4 * Corresponding author. Tel.: +1 403 220 4951; fax: +1 403 282 8249. E-mail addresses:[email protected] (G. Ho), [email protected] (C.T. Scialfa). Search for other works by this author on: Oxford Academic Google Scholar Interacting with Computers, Volume 17, Issue 6, December 2005, Pages 690–710, https://doi.org/10.1016/j.intcom.2005.09.007 Published: 06 October 2005
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".