Perspectives on and from Institutional Ethnography
Bibliographic record
Abstract
His doctoral research examines how higher education, academic judgement of quality, metrics and university rankings are co-produced.He is interested in the sociology of knowledge -quantification and categorization, in particular, surveillance, organizations, health, mental health and illness.He considers himself a generalist social scientist, familiar with social theory, quantitative, qualitative, and network analytic methods and uses these to answer questions in both applied and academic contexts.Jo Bishop is a senior lecturer in Childhood Studies at the University of Huddersfield.She has worked in post-compulsory education for around 25 years teaching across a range of vocational courses which prepare people for employment in schools, colleges, social care, and youth work settings.Her current research interests lie in the enactment of policies which have resulted in a more diverse schools workforce, including the introduction of occupational roles not previously associated with this arena such as the police.The subject of her recent PhD thesis focused on the experiences of learning mentors in English secondary schools as an example of paraprofessionals who have an increasing presence in formal education settings and are required to perform a qualitatively different role to that of teachers.Jo is currently planning research which will look at how processes and systems of pastoral care are conceived and implemented within an increasingly fragmented school system in the UK.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| 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".