An exploratory examination of racial cyberbullying among undergraduate students at McGill University
Bibliographic record
Abstract
Examples of racial discrimination are plentiful within Canadian history, as well as within institutions of higher learning. Virtual experiences of racial discrimination, similar to their physical world counterparts, are better understood within the social and political context in which the encounters take place. The purpose of this study is to: 1) examine the prevalence of general cyberbullying; 2) examine the prevalence and nature of racial cyberbullying; 3) determine the relationship between racial cyberbullying and gender, socioeconomic status, and ethnicity. Thirty-eight undergraduate students from McGill University participated in the study. The results from the analysis suggest that general and racial cyberbullying are prevalent within this sample of McGill students. The most frequently reported perceived motivators for racial cyberbullying are bullying because of one's God(s) (15.8%), name (10.5%), and language spoken (10.5%). A series of Fisher's Exact Tests were conducted to examine the relationship between individual characteristics and racial cyberbullying. Results from the analyses suggest a statistically significant relationship between gender and reporting victimization of racial cyberbullying. The results from this study, although exploratory, have implication for the recommendations put forth by the McGill University Principal's Task Force on Diversity, Excellence and Community Engagement. Specifically, the results can help guide the development and implementation of support programs for staff and students at McGill. Limitations of the study are also discussed.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".