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Record W2077106974 · doi:10.1080/0142569042000205145

Politics, policies and practice: assessing the impact of sexual harassment policies in UK universities

2004· article· en· W2077106974 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBritish Journal of Sociology of Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHarassmentPoliticsSociologyPolitical sciencePublic administrationGender studiesLaw

Abstract

fetched live from OpenAlex

Abstract Since sexual harassment was first named and identified as an obstacle to women's equality in the mid 1970s, concern about both its prevalence and its damaging effects has resulted in the widespread introduction of anti‐harassment policies in UK universities, as in other work and educational settings. The study reported here sought to assess the impact of these policies, in the context of criticisms of the effectiveness of universities' implementation of equal opportunities policies more generally. Its findings indicate that key differences in how policies are both conceived and implemented influence the degree of impact they have. In discussing these different approaches to tackling harassment I highlight the significance of adopting a 'proactive', rather than merely 'reactive' strategy and consider the reasons why many universities appear to be resisting doing so. Notes * Department of Sociology, University of Victoria, Victoria, B.C., Canada V8W 3P5. Email: thomasam@uvic.ca Although men too may be subject to harassment, they are much less likely to encounter it than women (Collier, Citation1995). This is an organization established in 1992 to provide support for those involved in developing and implementing anti‐harassment policies. The second phase of this research project consisted of a detailed study of the impact of the introduction of a harassment policy in three universities in each country. This research was funded by a Research Fellowship awarded by the Leverhulme Trust (ref. RF&G/7/9500349). This sample consisted of all publicly funded UK institutions of higher education which at that time were recognised as universities, including each of the constituent colleges of the University of London and the University of Wales. It should be noted that the numbers of cases reported are not assumed to reflect the 'actual' incidence of harassment in any given university, since incidents of sexual harassment are—like many other forms of abuse or assault—massively under‐reported (Brooks & Perot, Citation1991; Schneider et al., Citation1997). The mean number of cases reported was 8.8, with a standard deviation of 8.42. With such an erratic distribution the most meaningful way in which to analyse these data proved to be by simply grouping them into two categories—those reporting five or more cases and those with fewer than five. In fact, it is worth noting that there was no obvious relationship between university size and the number of cases reported. Since only 32 universities provided data on this, cell sizes are in some cases too small for it to be possible to make any claims for statistical significance. However, rather than automatically dismissing such data without question, Leffler and Gillespie (Citation1987) propose that we should view findings such as these as 'heuristically useful' in guiding the development of subsequent research in the field. I was told by an informant from one particular university that it had adopted and later abandoned the informal 'network' approach, not merely for reasons of cost, but also, apparently, on account of fears that the numbers of contacts being recorded were making it look as though sexual harassment was rife there, by comparison with institutions with a more formal reporting procedure, where numbers were considerably lower. Additional informationNotes on contributorsAlison M. Thomas Footnote* * Department of Sociology, University of Victoria, Victoria, B.C., Canada V8W 3P5. Email: thomasam@uvic.ca

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.445
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it