Lifelong learning and the counter/professionalisation of childcare: A case study of local hybridizations of global European discourses
Bibliographic record
Abstract
We provide a historical (genealogical) study of the changes in discourses on adult education since the famous UNESCO conference in Montreal, to present day texts of the European Union on lifelong learning. We also analyse how these changing global discourses on lifelong learning have travelled – through the hegemony of English language – to local situations, such as in Flanders. In the case of Flanders, they have paradoxically contributed to a significant counter-professionalisation of the early years workforce. This genealogical case study also shows how research, policy and practice are closely intertwined in their contribution to this paradox. The study shows that genealogical approaches are useful to show both how international influences need to be considered in a globalised world, but also how specific local ‘hybridisations’ of these discourses are constructed.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| 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".