Shifting the pendulum on gender equality and social inclusion through emerging approaches to citizenship education
Bibliographic record
Abstract
This paper illustrates how particular approaches to citizenship education can be catalysts or deterrents in the promotion of gender equality and social inclusion. The review suggests that new, emerging (i.e. decolonising, feminist, intersectional/situational) approaches to citizenship education and learning are more likely to address the multiple experiences of women, racialised, LGBTQ+ and disenfranchised groups, particularly when intersectional frameworks and situated learning are included. It further suggests emerging approaches infused with values and practices that enhance solidarity and social inclusion might be more likely to result in change rather than continuing with lifelong learning that emphasise credentials, individual economic growth, and limited scopes on literacy. Using a literature review method, global examples are provided that illustrate how inequalities are ameliorated and exacerbated, for example by the COVID-19 pandemic. The review suggests that emerging approaches intimate multiple benefits because they move beyond neoliberalism, engage with non-Western ways of knowing, and view all gender conforming, non-conforming and disenfranchised populations as change agents capable of solidarity and participatory democracy.
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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 | 0.022 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".