Beyond the trinity of gender, race, and class: Further exploring intersectionality in adult education
Bibliographic record
Abstract
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 intersection of gender, race, and class. Meanwhile, intersectionality theories are more diverse, and include discussions of social structures, geographies, and histories that serve to build richer, nuanced descriptions of how privilege and oppression are experienced. Because the purpose of intersectionality is to understand how social identities and positions are constructed and to challenge the structures of power that oppress particular social groups, this approach is important for feminist and social justice educators. We, the Canadian authors of this manuscript, posit that adult education should move beyond intersectionality that focuses only on the trinity of gender + race + class to consider the other inequalities and the true complexities of representation and collective identities. By exploring literature in feminism, adult education, and intersectionality, we illustrate a gap at the core of adult education for social justice. We draw upon two examples of national research with and by the Canadian Research Institute for the Advancement of Women to illustrate how intersectionality is understood and works in practice. (DIPF/Orig.)
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".