Improving learning and practice in the workplace through living theory research
Bibliographic record
Abstract
Keeping the goals of IAL at the forefront of this keynote I shall focus on the use of Living Theory research and Action Research in raising capabilities, catalysing innovation, and leading research in workforce learning. I shall provide access to the evidence that demonstrates that a Living Theory approach to Action Research can facilitate the development of an effective, innovative and responsive CET sector that is able to meet the needs of industries and the workforce (see http://www.ial.edu.sg/). The evidence includes the use of multimodal narratives with digital technologies in workplace learning that have been accredited for higher degrees in continuing education and training and which are freely available from the Internet . The evidence will be drawn from the living - theories produced by individuals as they explore the implications of asking, researching and answering questions of the kind, �How do I improve what I am doing in my workplace practice?� The international significance of this evidence will include living theories from workplaces in Si ngapore, South Africa, Canada,Europe, China, South Africa and Japan.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.014 |
| 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".