A Tender Revolution: An Exploration of Multiple Marginalization and Identity
Bibliographic record
Abstract
Feminist standpoint theory (FST) centres the object of psychological research inquiry upon the systemic power relations that enact discrimination, violence, and inequality. By continuing to enrich Canadian counselling psychologists’ understandings of these relations of sociocultural power, researchers and clinicians alike can better appreciate and respond to the ways that they produce unique experiences of stress, especially for people who hold multiple marginalized identities. In this dissertation, multiple-marginalization (MM) refers to the interlocking nature of systemic power relations that lead to structural and person-to-person behavioral manifestations of bias against a particular group; for example, oppression. Since the 1950s, identity development has been a prominent area of research in psychology, leading to the development of numerous conceptual models in response to differing perspectives and advancements in civil rights movements. Although at times an uneasy partnership, this combination of on-the-ground and academic work has served to mutually influence wider thinking about the constructs of identity and wellness. In this dissertation, I critically explore how the field of counselling psychology approaches identity development. In relation, there is a lack of counselling psychology research that investigates the experiences of identity development under the influences of MM using an intersectional and social justice perspective. As a result, my aim was to create a body of work that might invigorate counselling psychology (and allied professions) in recognizing and working in solidarity with clients/patients, research participants, and community members against the insidious forces of MM.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.051 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".