Bibliographic record
Abstract
This publication is the second of two newsletters published in this issue of Art/Research International. This newsletter is followed by a commentary and references for both newsletters. Funding from Research Impact Canada, VP Research & Innovation University of Alberta and the Kule Institute for Advanced Study mobilized evidence-informed knowledge from “Image, Body, and Voice: Supporting Girls’ Sense of Wellbeing,” a participatory poetic inquiry with grade-6 girls in an inner-city school in Alberta, through professional community engagement. At an afternoon workshop, held during spring break with in- service teachers, leaders, and parents/guardians, activities central to the research were shared for the goal of generating mutual benefit skills and knowledge. It offered experiential opportunities, including the creation of mini body maps, and a combination of strategies to support mental wellness, including culturally aware methods for diverse populations, intended for social inclusion and freedom from discrimination and violence. Newsletter 2, as research creation artifact, sought to support teachers, leaders, and families during the onset of COVID-19, when K-12 education moved to on-line delivery and health regulations required social distancing. The content shared beyond “Image, Body, & Voice” sought to support school staff and families through information about compassion fatigue, soul weariness, the power of play, the centrality of the body in healing, emotional regulation and traumatic events. This newsletter is one of two research documents provided as follow up to the attendees of both funded events.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.361 | 0.147 |
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".