Researching inequality and lifelong education from 1982 to 2020: A critical review
Bibliographic record
Abstract
This article explores the International Journal of Lifelong Education archives in the period 1982–2020. We analyse how the Journal engages with the issue of inequality. This is accomplished by systematically identifying relevant articles within the archives, and reviewing these whilst taking account of the societal, cultural and political or economic contexts in which they were written. Most articles identified for review focused on specific disadvantaged groups, discussing ways in which adult education might help, support and strengthen them. A minority took a more critical approach, assessing the drivers for inequality, or problematising the role of lifelong education as a catalyst for addressing inequality or social injustice. In our analysis, we distinguish between inequalities related to class, gender and migration/ethnicity as themes emerging from our initial sweep of the archives, however these themes are represented unequally both in terms of number and attention given across the decades. Perhaps surprisingly, given the different forms of inequality addressed in the Journal, it seems that only very few of these papers can be directly associated with historical events and contexts relevant to the times in which they were written. Theoretically driven conceptualisations of inequality are rarities within the archives, with some notable exceptions.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".