Interview with Barrie Irving and Beatriz Malik
Bibliographic record
Abstract
Barrie Irving has a research and teaching background in careers. Until recently he was a Senior Visiting Research Fellow, Department for Career and Personal Development, Canterbury Christ Church University College, UK. He has written widely on social justice and related issues and recently co-authored In good faith: Schools, religion and public funding, published in 2004 by Ashgate. With Beatriz Malik, he has also co-edited Critical reflections on career education and guidance. Promoting social justice within a global economy, published by Routledge-Falmer in 2005. Beatriz Malik is Assistant Professor in Educational and Career Guidance at the National University of Distance Education (UNED) in Madrid. Her fields of research and teaching include intercultural education, social mediation and counsellor qualifications. She is also interested in multiculturalism in guidance, career guidance programs (specifically how they address career diversity), students with disabilities and the promotion of social justice. In this interview, the AJCD discusses a range of issues with career researchers and writers, Barrie Irving (BI) and Beatriz Malik (BM).
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".