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Record W4310054407 · doi:10.1080/02601370.2022.2148303

Researching inequality and lifelong education from 1982 to 2020: A critical review

2022· review· en· W4310054407 on OpenAlexaff
Petya Ilieva-Trichkova, Sarah Galloway, Bernhard Schmidt‐Hertha, Shibao Guo, Anne Larson, Vicky Duckworth, Tonic Maruatona

Bibliographic record

VenueInternational Journal of Lifelong Education · 2022
Typereview
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLifelong learningInequalitySociologyPedagogy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.119
GPT teacher head0.548
Teacher spread0.428 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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