Classroom Incivility, Gender, Race, and Indigeneity in Higher Education: Faculty Perspectives on Social Factors and Identity Markers
Bibliographic record
Abstract
Based on 20 semi-structured interviews with faculty members from a mid-sized university in Western Canada, this paper offers an examination of research participants’ experiences and perceptions of classroom incivility, particularly those that are shaped by social factors such as identity markers (race, ethnicity, gender) as well as cultural beliefs regarding what is considered politically sensitive subject-matter (in this case, indigeneity). Data analysis reveals that when research participants detect instances of incivility expressed as resentment around race, gender, and indigeneity, they struggle to find a balance between taking up the teaching moment and maintaining safe space. This paper offers a reflection on the extent to which research participants choose to assume intellectual candor when making sense of incivility. Pedagogical responses are highlighted in an effort to recognize the importance of a critical consciousness about social positioning, race relations, power, and privilege. Key words: Incivility, Race, Gender, Indigeneity, Higher Education. Reposant sur 20 entrevues semi-structurées avec des membres du corps professoral d'une université de taille moyenne dans l'Ouest canadien, cet article propose un examen des expériences et des perceptions des participants à la recherche en matière d'incivilité en classe, particulièrement celles qui sont façonnées par des facteurs sociaux tels que les marqueurs d'identité (race, ethnicité, sexe) ainsi que les croyances culturelles concernant ce qui est considéré comme un sujet politiquement sensible (dans ce cas, l'indigénéité). L'analyse des données révèle que lorsque les participants à la recherche détectent des cas d'incivilité exprimés sous forme de ressentiment autour de la race, du genre et de l'indigénéité, ils luttent pour trouver un équilibre entre la prise en charge du moment d'enseignement et le maintien d'un espace sûr. Cet article propose une réflexion sur la mesure dans laquelle les participants à la recherche choisissent d'assumer la franchise intellectuelle lorsqu'ils donnent un sens à l'incivilité. Les réponses pédagogiques sont mises en évidence dans le but de reconnaître l'importance d'une conscience critique du positionnement social, des relations raciales, du pouvoir et des privilèges. Mots clés : incivilité, race, genre, indigénéité, éducation supérieur
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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 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".