Making Masala: Shaping a Multiperspectival Narrative Inquiry through a Re-search Of and For Storied Images
Bibliographic record
Abstract
Engaging in a multiperspectival narrative inquiry alongside South Asian girls, their mothers, and teacher over time (in/outside school spaces), I inquire into our curriculum-making experiences in the worlds we traverse, occupy, and live. Co-participants narrate stories of (be)longing and identity, whilst juxtaposing profound tensions between their personal knowledge and the knowledge valued within their experiential worlds. I recall moments which bring into various focus- personal, familial, cultural, institutional, linguistic, and social narratives whose plotlines traverse the geographical locales of Canada and South Asia, and less visible plotlines composed within the intersections of the heart and mind. These are masala moments replete with collaged images which re-frame my identity as a South Asian, an Indian, a Canadian, a daughter, a student, a teacher, and as a researcher across ever-shifting points of time and context. These curriculum-making encounters composed of narratively thinking and working within arts-informed pieces, contour my understandings of what it means to court possibility. Inviting for the potentiality of arts within narrative inquiry, I purposely advocate for a multiplicity of stories to be shared between You and I, in the hopes of opening he art -full ways of thinking, learning, and being alongside one another.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.051 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".