The Teaching Roles, Institutional Locations, and Terms and Conditions of Employment of Part-time Teachers in UK Higher Education
Bibliographic record
Abstract
This article is devoted to a phenomenon of increasing relevance in contemporary higher education in the UK-the growing resort, especially noticeable since the early 1980s, to using part-time adjunct teachers for the performance of conventional teaching duties. It offers a typology to comprehend the variety of such teachers, including many who are not individually enumerated by the Higher Education Statistics Agency (HESA), and it discusses two distinctive employment relationships that differentiate between part-timers. It describes some of the existing studies of part-time teachers in UK higher education, as well as drawing analogies to the better-developed North-American literature on the equivalent topic there. The article also describes a telephone survey conducted in mid-/late-1997 among personnel officers in an achieved sample of 22 'old' and 'new' institutions in order to ascertain information on the approximate numbers of particular types of part-timer at each institution and on the terms and conditions of their employment. It presents information on the relative prevalence of various types of part-time teacher according to type of university (including whether 'old' or 'new' sector) and to further institutional characteristics. It discusses, on the basis of the collected data, sector-specific employment practices concerning part-time teachers and, from information about the terms of their employment, it assesses the degree of their contractual disadvantage in relation to conventional full-time staff.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".