Your Hair is Your Beauty: Jamaican-Canadian Women's Cross Cultural Hair Experiences
Bibliographic record
Abstract
This thesis study examines the interrelated hair stories and migratory experiences of first-generation Jamaican-Canadian women who were born in the Caribbean and who now live within the Greater Toronto area (GTA). My study employs an intersectional framework to explore the experiences of Black women whose African, Caribbean, and Black Canadian diasporic hair identities prove to be manifestations of lived experiences of anti-Black racism and traumas identified as hair discourses specifically associated with the Black woman experience. Integral in this framework is the examination of Black hair as social positioning, demonstrated in its use in creating Black women’s identity and the rootedness of these identities within migration and re-location within the Canadian diaspora. Black feminist thought is used throughout as a critical foundation and grounding theory that aligns with the Black hair experience. By using theoretical works within Black feminist thought and anti-Blackness discourses, this study addresses how the creation of Black hair identities are linked and, through colonial historical dominance, have morphed into a normalized misrepresentation of white beauty ideals. The six study participants have similar geographical migration histories that serve to enrich the principles of Black feminist thought. Their identities and hair stories merge in the creation of Black womanhood and the intricate connective tissue of self-esteem building/creation, and empowerment. The analysis engages with discussions of Blackness, belonging and migration while aiming to address marginalization and hair culture stigmas. This study concludes with an assessment of the invisibility, assimilation and survival of Black women’s beauty and identity through an enlightenment and empowerment of a unique and collective consciousness that Black women share. Additionally, I conclude that researching the politics of Black women’s hair is vital to increase the voice, visibility and validity of representation across and throughout the Canadian Black diaspora.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.044 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".