Learning about workplace learning and expertise from Jack
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight some methodological problems concerning the neglect of participants' voices by workplace ethnographers and neglect of the highly interactional and co‐constructive nature of research interviewing. The study aims to use discourse analysis, to show the phenomena of workplace learning and expertise to be constituted in participants' talk. Design/methodology/approach From excerpts of natural talk and research interviews by fish culturists speaking about their learning in a salmon hatchery, discourse analysis is used to analyze how workplace learning and expertise are rhetorically performed. Findings The paper finds that fish culturists drew on two discursive repertoires/resources – school‐ and workplace‐based learning – to account for their learning and expertise. The main participant affirmed the primacy of interest and practical workplace experience in his job just as he presupposed a weak correlation between school‐based (theoretical) and workplace (practical) knowing. However, both kinds of learning were deemed important though articulating this view depended on the social contexts of its production. Research limitations/implications Discourse analysis does not establish immutable truths about workplace learning and expertise but rather it is used to understand how these are made accountable through talk in real‐time, that is, how the phenomenon is “done” by participants. Practical implications There is increased sensitivity when using ethnographic and interview methods. No method can avoid being theory‐laden in its conduct and reporting but discourse analysis perhaps does it better than its alternatives. Originality/value While some contributors to this journal have also approached workplace learning from a discursive perspective, this paper attempts to understand the phenomenon solely from participants' categories and interpretations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".