The Enigma of Adult Bullying in Higher Education: A Research-Based Conceptual Framework
Notice bibliographique
Résumé
Introduction Bullying is an abusive behavior that, undoubtedly, has had a long history and is quite pervasive in contemporary society. However, a review of the extant literature clearly shows sparse research attention devoted to the study on the nature of bullying of adults by adults (see Rodkin et al., 2015; Swearer & Hymel, 2015). Within this context, the major investigatory focus has been on bullying experiences in workplace settings (Nielsen & Einarsen, 2012). At the same time, emerging research on adult bullying in educational settings has recently appeared in the literature, as teachers can become prime targets of incivility, retaliation, and harassment (Fox & Stallworth, 2010). The aim of the current study is two-fold: a) to review emerging research on bullying and related incivility by adults in higher education settings; and b) to offer a conceptual lens to guide current understanding and future research, based on salient findings across 5 topical areas of study related to the phenomenon of interpersonal mistreatment and harassment, i.e., destructive leadership, abusive supervision, workplace bullying, incivility, and the Adult Bully Syndrome. To our knowledge, no systematic analysis of this type has been presented to date on this neglected area in educational research. Research on Bullying in Higher Education Historically, there has been limited empirically-based research on bullying in higher education settings (Hollis, 2012). Actually this is not surprising given the top-down organizational structure of colleges and universities (Twale & De Luca, 2008). Moreover, even fewer studies have provided a conceptual framework to advance research efforts specific to bullying by adults in the academic setting (see Keashly & Neuman, 2010). Undoubtedly, the impact of incivility in academic settings can have onerous repercussions both for employees (in the form of humiliation, resentment, demoralization) and on institutional climate (productivity, collegiality, faculty retention) (Raskauskas & Skrabec, 2011). Sadly, in escalated form, groups of individuals (a.k.a. Mobbing) can conspire and coordinate attacks on a specific victim (Keim & McDermott, 2010). In a major investigation, Keashly and Neuman (2010) review seminal research findings regarding bullying in higher education, with a focus on contextual antecedents to interpersonal incivility, in the form of both covert and overt aggression. Prevalence Statistics In a dissertation study, Mourssi-Alfash (2014) examined the relationship between workplace bullying and organizational justice among faculty and staff at a university in the Midwest. Based on data from 786 respondents, 35% confirmed that they had been bullied, with females reporting the highest incidence rate. Thomas (2005), in a study on bullying at a large university in the United Kingdom, found that 45% of support staff employees reported being bullied and 40% witnessed colleagues being bullied. In line with these prevalence rates, McKay et al. (2008) reported that nearly half of college faculty experience bullying in the work setting lasting more than 3 years. Interactive Communication Technology With advent of the Internet and advancements in interactive communication technologies, unwanted hostility and interpersonal intimidation in the form of cyber-bullying has become evident in higher education settings (Kowalski et al., 2012; Piotrowski & Lathrop, 2012; Schenk et al., 2013). In fact, due to the ease of antagonizing others, educators can experience cyber-bullying by supervisors, administrators, staff, fellow instructors, and even college students (e.g., Barlett & Gentile, 2012; Broster & Brien, 2010). In a study involving 121 faculty members at a Canadian university, Cassidy et al. (2014) found that 17% experienced cyber-bullying over the prior year. In a dissertation investigation involving 56 faculty at a liberal arts university in Hawaii, Vance (2010) revealed that 39% claimed that they were targets of cyber-harassment. …
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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 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| 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) | 0,002 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».