Using Grounded Theory to Explore Learners' Perspectives of Workplace Learning.
Bibliographic record
Abstract
Grounded theory is an inductive enquiry that explains social processes in complex real-world contexts. Research methods are cumulative cyclic processes, not sequential processes. Researchers remain theoretically sensitive and approach data with no preconceived hypotheses or theoretical frameworks. Literature is reviewed as lines of enquiry and substantive theories emerge. Interviewers ask broad open questions, check understanding and prompt further description. Participants choose how they share their perspectives and experiences. Everything is considered data. Data is analyzed in cyclic processes. Initially coding uses participants' words, and then identifies patterns, social processes and emerging substantive theories. Memos and diagrams facilitate understanding of data and literature. Grounded theory is a suitable research methodology for work-integrated learning because grounded theory explains social processes, such as learning, in complex real-world contexts, such as workplaces, where multiple influencing factors occur simultaneously. A case study illustrates how grounded theory was used to explain learning in the workplace.
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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 | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".