The Online Learning Environment: Creating a space for women learners?
Bibliographic record
Abstract
This paper examines how online distance education acts to democratize access to, and suit the ontologies of, Mauritian women who seek to empower themselves for development. Data from semi-structured interviews of 30 middle class couples are presented in this paper. Interviews and analyses are premised on a feminist perspective and conducted within the social relations analysis framework. The objective of this research was to understand what types of supportive environments (social spaces) enable Mauritian women to engage in educational endeavours that promote their personal potentials and creativities which, in turn, advance democracy for all citizens of Mauritius. Husbands were also interviewed to provide ground for analysis and to decrease bias, which can be generated by women-only data. (1) Marriage/ family and (2) occupation, represent the ‘social spaces’ selected for this study. Discretion, degree of learner control, and the outreach capacity inherent in distance learning makes the online modality a natural choice to democratize women’s access to education. Based on interviewees’ experiences and perceptions, this study concludes that online learning can enhance and democratize women’s access to education for personal development – but only if the power relationships in the two ‘social spaces’ are well understood and well negotiated by these women. The findings in this paper shed light on the importance of understanding ‘learner spaces’ when establishing and setting-up open learning organisations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".