Designing interfaces that support formation of cognitive maps of transitional processes: an empirical study
Bibliographic record
Abstract
Journal Article Designing interfaces that support formation of cognitive maps of transitional processes: an empirical study Get access Kamran Sedig, Kamran Sedig ⁎ a Department of Computer Science and Faculty of Information and Media Studies, The University of Western Ontario, Middlesex College, London, ON N6A 5B7, Canada ⁎ Tel.: +1 519 661 2111x86612; fax: +1 519 661 3506. E-mail address:sedig@uwo.ca (K. Sedig). Search for other works by this author on: Oxford Academic Google Scholar Sonja Rowhani, Sonja Rowhani b Department of Computer Science, The University of Western Ontario, Middlesex College, London, ON N6A 5B7, Canada Search for other works by this author on: Oxford Academic Google Scholar Hai-Ning Liang Hai-Ning Liang b Department of Computer Science, The University of Western Ontario, Middlesex College, London, ON N6A 5B7, Canada Search for other works by this author on: Oxford Academic Google Scholar Interacting with Computers, Volume 17, Issue 4, July 2005, Pages 419–452, https://doi.org/10.1016/j.intcom.2005.02.002 Published: 27 April 2005 Article history Received: 09 March 2004 Revision received: 15 October 2004 Accepted: 21 February 2005 Published: 27 April 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 | 0.007 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".