Opening Up the Continuing Professional Development Imagination: Bringing the Clinical Workplace Into View
Bibliographic record
Abstract
ABSTRACT: This Foundations paper introduces the "Working as Learning Framework (WALF)" to the continuing professional development community. Developed by researchers in the domain of workplace learning, the WALF draws upon theories and concepts from economics, sociology of work, and sociocultural theories of learning. The Framework provides conceptual tools to analyze interconnections between workplaces, the organization of work tasks, and learning. Through these interconnections, the Framework introduces the concepts of "expansive learning environments" and "restrictive learning environments." This paper provides an overview of the WALF before discussing possible implications for continuing professional development educators and researchers. Ultimately, this Foundations paper invites readers to engage with the rich scholarship on workplace learning informed by sociocultural concepts of learning and complemented by research on work and workplaces.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| 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".