Family Photos: Digital photography as Emancipatory Art Education in Montreal’s Black Community
Bibliographic record
Abstract
This thesis documents a participatory action research project in which I collaborated with a Caribbean-Canadian family of four, to study their experiences of familial art education and photographic practice, and to generate recommendations for Emancipatory Art Education in Montreal’s Black community. Emancipatory Art Education (EAE) is an emerging approach to Black community art education that I situate among African Diaspora traditions of ‘education for liberation’ and critical multiculturalism discourses in the field of art education. Family Photos begins a long-term participatory research practice aimed at defining and developing EAE theory and practices for the community from within the community. An autoethnographic study through which I locate and situate my identities as a Black Montrealer consequently emerges as a critical component of this work. \n \nThrough studying and practicing photography as art, family members develop technical skills and inclusive understandings of art, while increasingly expressing their own individual and collective aesthetic identities. All express affirmative feelings about the project and a desire to participate in similar projects in the future, and thus conclude that family art practice can be a positive and engaging practice for other families and members in the Black community. Our results emphasize photographic practice as a site for exploring issues of identity, race and representation, and tensions between the private and the public. Recommendations are geared toward EAE and address familial and intergenerational community art education; photography, ethics and boundary control; and participatory action research in community art education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.002 |
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".