A Story Cloth of Curriculum Making: Narratively S-t-i-t-c-h-i-n-g Understandings through Arts-Informed Work
Bibliographic record
Abstract
Amidst the diverse worlds () we traverse, inhabit, and live within, traditional epistemic and ontological considerations can privilege certain ways of knowing and being whereupon hegemonic narratives may become perpetually fashioned. The corollary is then, an (un)intentional neglect of other stories. Likewise, stories patterned along stereotypical lines () can become sites of default when (dis)engaging with a multiplicity of voices and more worrisomely, an excuse employed by some, to dehumanize. Drawing upon my experiences as a Canadian South Asian female doctoral student, engaging in the ethical and relational methodology of narrative inquiry, I ruminate upon certain curriculum-making experiences () with voice and query how to go about humanely imparting voice. Framing reminiscences and musings as storied swatches, I autobiographically share pivotal moments leading up to my art-making choice of stitching a story cloth to communicate and re-present knowledge in one university course. These curriculum-making encounters (amongst others) composed of narratively thinking and art-making () continue to interweave my understandings of educational research and what it means to be a researcher learning alongside co-participants. Inviting for the potentiality of arts () within narrative inquiry may work to unravel borders between the You and I, and Us and Them positioning that can shape everyday interactions. This chapter purposely advocates for an enhanced openness to heterogeneous meaning-making processes and re-presentations of knowledge. In doing so, the hope is to metaphorically stitch heart-full ways of communicating, 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".