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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".