Using Interactive Technology to Disseminate Research Findings to a Diverse Population
Bibliographic record
Abstract
This paper demonstrates how case stories can be used to disseminate the findings of several case studies on negotiating accommodations in the workplace. It highlights the power of interactive technology and of the partnership between the researchers and the Canadian Council for Rehabilitation and Work (CCRW). The paper describes the process of designing an interactive web-based case story for the purpose of disseminating research findings. The interactive case story is an extension of both the case study and the narrative case story. As part of a larger research project, it is our goal to use interactive case stories to investigate the impact of essential skills training on workers with disabilities who negotiate with employers for workplace accommodations. Résumé Le présent article montre comment les histoires de cas peuvent être utilisées pour diffuser les conclusions de plusieurs études de cas sur la négociation entourant l’aménagement du milieu de travail. Il met en évidence le pouvoir de la technologie interactive et du partenariat entre les chercheurs et le Conseil canadien de la réadaptation et du travail (CCRT). L’article décrit le processus de conception d’une histoire de cas interactive en ligne visant à diffuser des résultats de recherche. L’histoire de cas interactive est un prolongement à la fois de l’étude de cas et du récit de l’histoire de cas. Dans le cadre d’un plus vaste projet de recherche, notre but est d’utiliser des histoires de cas interactives pour étudier l’impact de la formation sur les compétences essentielles chez les travailleurs handicapés qui négocient avec leur employeur pour l’aménagement de leur milieu de travail.
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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 | 0.063 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".