Bibliographic record
Abstract
Using two silent-dialogue scenarios as a basis for discussion, this chapter provides an overview of how workplace learning can be framed by transformative learning theory. Based on the literature on workplace learning, the authors review the primary kinds of workplace learning that can be found in diverse workplace contexts. In contrast to the debates occurring in adult education about what is and what is not transformative learning, here they suggest that each kind of workplace learning has the potential to be a transformative learning experience. The chapter concludes with a discussion of paradoxes and implications. In this chapter the authors explore the nature of workplace learning from the perspective of transformative learning theory. In order to do this, they present two scenarios, one related to employer-sponsored learning in the workplace, and one related to leadership development facilitated by an external consultant. For each scenario, the authors use a silent dialogue—revealing the thoughts of the educator as the scenario unfolds, and the thoughts of one of the participants during the same timeframe. The silent dialogues reveal the conflicts and issues inherent in the scenarios. Drawing on the literature on workplace learning, the chapter provides an overview of kinds of workplace learning, and then analyzes the first scenario. This is followed by the presentation of the second scenario and an analysis of that scenario, next turning to transformative learning theory, and using that framework to better understand the kinds of workplace learning and how they can be transformative. The chapter discusses the paradoxes inherent in applying transformative learning theory to workplace learning and lists some implications for practice, theory development, and research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".