Bibliographic record
Abstract
Racial microaggressions are commonplace, brief, subtle denigrating messages targeted at people of colour because they belong to a racial minority group (Pierce, Carew, & Pierce-Gonzalez, 1978; Solórzano, Ceja, & Yosso, 2000; Sue, 2010; Sue, Capodilupo et al., 2007). Because of their subtle and nebulous nature, these incidents may require complex coping strategies (Lewis et al., 2013; Noh, Kaspar, & Wickrama, 2007; Pierce, 1995). In this dissertation, I examined responses among racialized university students and community members in Montreal, Canada. Research questions were as following: (a) how do individuals who experience racial microaggressions respond to or cope with racial microaggressions?; (b) what effects, if any, do intersecting social group identities (e.g., race, ethnicity, gender, and social class) have on individuals’ responses to racial microaggressions?This dissertation comprises two studies. In study one, I conducted focus groups with five groups of students (i.e., East Asian, South Asian, Black, and Arab Canadians and Aboriginal students; n = 32) at a predominantly White Canadian university. To analyze the data, I used the consensual qualitative research approach (CQR; Hill et al., 1997, 2005) that has been utilized often in microaggression research (e.g., Sue, Capodilupo et al., 2007, 2008). In study two, I conducted individual interviews with Black Canadian (n = 5) and Indigenous (n = 5) community members who pursued employment directly following secondary education. I utilized interpretative phenomenological analysis (IPA; Smith, Flowers, & Larkin, 2009), a complementary qualitative approach to augment my earlier investigation. Across both the student and community samples, findings demonstrated that participants used a range of collective, protective, and resistant strategies in response to experiences with racial microaggressions. At times, participants’ strategies differed based on intersecting social group identities. Response strategies convey three important features: (a) the influence of racial/ethnic identity in the use of resistance; (b) deliberate strategies of disengagement as a form of resistance; and, (c) participants’ use of humour to serve different functions depending on the context. Finally, I offered directions for future research, re-conceptualization of responses to racial microaggressions, and practice.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".