Stories of Learning across the Lifespan: Life History and Biographical Research in Adult Education
Bibliographic record
Abstract
Life history or biographical approaches to research in lifelong learning may be particularly useful for researchers working from a social purpose and/or feminist perspective. Adult educators working from an emancipatory framework are often curious about factors that shape people's lives, both from an individualistic, biographical perspective and from a broader social-cultural framework. Through life history and biographical research they can gain insights into what motivates people to engage in adult learning for social and personal change. Looking at examples taken both from the literature and from four different research studies that the author has engaged in during recent years, some of the benefits, challenges, and possibilities for life history/biographical research are explored in this paper. Both practical aspects, such as how to recruit participants, and more philosophical/methodological concerns such as the importance of recognising how participants use narrative as a means to explore meaning making around their own identity and life course are explored in the discussion. In conclusion, the paper argues that new technologies and emerging forms of artistic representation in life history and biographical research offer opportunities for both educators and participants, as well as other audiences, to explore adult learning experiences connected to social justice.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 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".