To Know, To Love and To Heal: PhotoStory and Duo-Ethnography as Approaches to Enhancing Social Justice and Self-Actualization in High School Classrooms
Bibliographic record
Abstract
This article explores using PhotoStory to promote social justice in the classroom. Interweaving Photovoice (Wang & Burris, 1997) with story-sharing results in PhotoStory, a unique teaching and learning approach that can empower voices of marginalized high school students. Through PhotoStory, we explore possibilities for self-actualization in high schools, where a primary pedagogical goal is to disrupt inequitable social orders and change oppressive behaviors and perceptions. Coming to critical consciousness for both teachers and students is vital, leading to engagement in dialogical pedagogy (Mthethwa-Sommers, 2014). As bell hooks (1994) asserts, oppression emanates in and through differences in relation to sex/gender, class and race. Similarly, Freire (1970) highlights the critical role of literacy skills to equip those who are oppressed to speak truth to power. Contextualized by habitus (Bourdieu, 1986), creators of PhotoStory documentaries come to understand their own and others’ lived experiences, enhancing individual and collective empathy, and promoting healing, offering holistic ways to connect through culturally responsive learning, and flipped and flattened pedagogies. By applying duoethnography (Sawyer & Norris, 2013), three authors, two graduate students and one professor discuss and critique this art-based pedagogical method via experiences of utilizing PhotoStory as an experiential teaching and learning tool. Although the scholars are different in relation to age, status, gender and sexual identities, they are committed to exploring an ethic of care and pedagogical self-actualization that serves our need “to know, to love, and to heal”.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".