Politics, policies and practice: assessing the impact of sexual harassment policies in UK universities
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle