Learning to change? : the role of identity and learning careers in adult education
Bibliographic record
Abstract
Contents: Barbara Merrill: Introduction: Moving Beyond Access to Learning Careers and Identity - John Field: Learning Transitions in the Adult Life Course: agency, identity and social capital - Michael Tedder/Gert Biesta: What does it take to learn from one's life? Exploring opportunities for biographical learning in the lifecourse - Simon Warren/Sue Webb: Accounting for structure in agency: recursive methodology, social narratives and habitus - Paula Guimaraes/Amelia Vitoria Sancho: Fragments of Adult Educators' Lives: Reflecting on Informal Learning in the Workplace - Rob Evans: Talking of learning ... Auto/biographical narratives of learning and computer-assisted analysis of the language of professional discourse(s) in interviews - Patricia A Gouthro: Understanding Women's Learning Trajectories: Examining Life Histories of Women Learners in Canada - Tamsin Hinton-Smith: Lone parents as HE students: A qualitative email study - Rennie Johnston/Barbara Merrill: Developing Learning Identities for Working Class Adult Students in Higher Education -Nalita James/Bethia McNeil: The Impact of Drama on Young Offenders' Learning Identities and Careers - Peter Alheit: The symbolic power of knowledge. Exclusion mechanisms of the `university habitus' in the German HE system - Ewa Kurantowicz/Adrianna Nizinska: Practicing critical, reflexive and autonomous learning among students of higher education. A Polish case study - Ana Maria Ramalho Correia/Dulce Magalhaes de Sa/Ana Cristina Costa/Anabela Sarmento: Building a Knowledge and Learning Society in Portugal - Adult Students in Technological Schools and Higher Education Institutions - Jan Thorhauge Frederiksen: Grading and Knowledge - a Matrix of Student Identities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".