Bibliographic record
Abstract
Purpose – This chapter highlights two studies, one in Canada and one in the United Kingdom. The Canadian study focused on the examination of student experiences with respect to specific ‘difficult’ content in the classroom. The purpose of the study was to identify ways that were effective and engaging for students to learn. The UK study examined issues of access, retention and drop-out of non-traditional students in higher education. The study examined the learning experiences of women who returned to learning after being out of the education system for some time.Methodology – The Canadian study used surveys and interviews. Participants were recruited on the basis of their enrolment in specific classes. The UK study used interview samples drawn from student data in three universities. In each university, a cohort was followed and interviewed three times while in another cohort students were interviewed in their first year of study and different cohort in their final year of study.Approach – Both studies use a feminist, narrative approach that relies on reflexive engagement in the research process.Findings and implications – The studies highlight that the classroom is a place where dialogue and engagement occur; where the identities of the participants and their learning are in a dynamic process; and where the learners challenge attitudes and ideologies such as capitalism and forms of marginalisation. The studies revealed that learning has a social value and entreats women to reconsider their lives, work and citizenship.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".