Theories and methods for research on informal learning and work: towards cross-fertilization
Bibliographic record
Abstract
The topic of informal learning and work has quickly become a staple in contemporary work and adult learning research internationally. The narrow conceptualization of work is briefly challenged before the article turns to a review of the historical origins as well as contemporary theories and methods involved in researching informal learning and work. I review leading theoretical models by Livingstone, Eraut and Illeris, and summarize established methods in terms of case study, ethnographic and interview approaches, survey approaches and situated micro-analytic approaches. I argue that no single theoretical model or methodological approach has yet established dominance, and that these models and methods largely speak to distinctive, not wholly incompatible, features of the phenomena in question. I argue this suggests the potential for cross-fertilization of ideas is high.
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 | 0.100 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".