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Record W3209737892 · doi:10.3384//rela.2000-7426.3360

Beyond the Trinity of Gender, Race and Class

2021· article· en· W3209737892 on OpenAlexaffabout
Cindy L. Hanson, Amber J. Fletcher

Bibliographic record

VenueEuropean Journal for Research on the Education and Learning of Adults · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsIntersectionalityGender studiesOppressionPrivilege (computing)SociologyFeminismRace (biology)Black feminismFeminist theoryAdult educationClass (philosophy)EpistemologyPolitical sciencePedagogyPoliticsLaw

Abstract

fetched live from OpenAlex

Research exploring the gendered dimensions of adult learning has blossomed in the past two decades. Despite this trend, intersectional approaches in adult learning, research, and teaching remain limited primarily to the three categories of gender, race, and class. Intersectionality theory is more diverse than this and includes discussions of social structures, geographies, and histories that serve to build richer, more nuanced descriptions of how privilege and oppression are experienced. Because the purpose of intersectionality is to understand how social identities are constructed and to challenge the structures of power that oppress particular social groups, this approach is important for feminist and social justice educators. The Canadian authors of this manuscript posit that adult learning should move beyond intersectionality that focuses only on the trinity of gender + race + class in order to consider the nuances of inequality and the true complexities of representation and collective identities. By exploring literature in feminism, adult education, and intersectionality, they illustrate a gap at the core of adult education for social justice. Finally, they use two examples to illustrate how intersectionality works in practice.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.114
GPT teacher head0.435
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes2
Has abstractyes

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