Understanding solidarity: the role of emotions and inclusive victim consciousness among gender and ethnic/racial groups in Canada
Bibliographic record
Abstract
Oppressive and discriminatory systems, laws, and policies impact people collectively over many generations, such as Indigenous Peoples in Canada. Reconciling such harms requires a collective effort from many within a society, meaning it is important to understand who is likely to be a source of support and why. Certain groups, such as women and racialized people, are especially likely to express solidarity, yet the underlying reasons for this may differ. In this dissertation, I examined how gender and ethnic/racial background relate to intergroup solidarity and the potential drivers of these relationships: inclusive victim consciousness and emotional responses to injustice. This project included three studies. First, to ensure that the measures I used were psychometrically robust, in Study 1, I developed multi-item scales that measured several emotional domains. In an online study, 280 university students learned about discrimination toward Indigenous Peoples in the child welfare system and then shared how they felt. Using factor analyses, I examined, identified, and retained items to develop scales that measure the domains of love, anger, sadness, feeling sorry, and hope. Further, configural invariance testing suggested the factor structure was similar between gender and ethnic/racial groups. Using these scales, in Study 2, I examined the relationships among gender, ethnicity/race, inclusive victim consciousness, emotions, and solidarity among 352 university students. In Study 3, I examined whether findings generalized in a diverse national sample of 612 adults from across Canada. Using t-tests, correlational analyses, and path analyses, the general pattern of results from Studies 2 and 3 suggest that (1) women express stronger emotions than men when they learn about injustice, and some feelings, such as empathy and feeling sorry, in turn, predict greater solidarity; (2) Racialized participants feel a greater sense of inclusive victim consciousness and in some circumstances, stronger emotions than White participants, which may, in turn, predict more solidarity; and (3) of all emotions, empathy is a particularly strong predictor of solidarity, whereas anger is not a significant predictor once other emotions are accounted for. I end with reflections on strengths and limitations, applying an Indigenous lens to quantitative research, and theoretical and applied considerations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".