Bibliographic record
Abstract
This descriptive case study describes a collaboration between the Center for Continuing Education at Saskatchewan Polytechnic in Canada and an organization administering services to support Saskatchewan’s health system. Its purpose was to provide foundational knowledge and skills in the workplace by creating accessible, engaging, and purpose-driven micro-credentials for adjudicators and others working in insurance companies across Canada. In the client’s context, adjudicators are frontline staff responsible for administering group life, extended health care, dental, and disability income plans. The successful completion of the micro-credentials was driven by sustained client engagement during weekly virtual meetings and a shift from traditional course blueprinting to Action Mapping. This approach was enhanced by using Figma to visually represent the relationships among key design elements. This case study is particularly relevant for post-secondary institutions (PSIs) aiming to collaborate with industry partners on designing and developing learning solutions that address real-world workforce needs. It highlights a common challenge PSIs face when applying traditional design methods in industry contexts, outlines the criteria used to select a more suitable design approach, and provides illustrative examples. Ultimately, the study offers practical strategies to enhance collaboration with workplace learning clients by streamlining the design process and ensuring alignment with industry expectations.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".