The Enigma of Adult Bullying in Higher Education: A Research-Based Conceptual Framework
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".