Bibliographic record
Abstract
I present autobiographical writing and photographic imaging, an arts‐based research methodology, to understand my personal knowledge as a means to understand my professional knowledge. Using my passions for writing and photography, I explore the conflicts, opportunities, and purposes of using a visual form of arts‐based narrative inquiry from a postmodern perspective. I demonstrate that the relationship between arts‐based narrative inquiry and educator/researcher reflexivity and transformation allows educators to focus their passions towards a greater understanding of what it means to teach. Keywords: arts‐based research; narrative research; personal, professional knowledge development L’auteure présente une méthode de recherche axée sur les arts, laquelle fait appel à des écrits autobiographiques et à des photographies, autant de façons pour elle de pencher sur ses connaissances personnelles afin de comprendre ses connaissances professionnelles. Passionnée d’écriture et de photographie, l’auteure explore les conflits, les occasions et les buts liés à l’utilisation d’une forme visuelle de recherche narrative dans une perspective postmoderne. Elle démontre comment la relation entre la recherche narrative, la réflexion et la transformation de l’enseignant/chercheur permet aux éducateurs d’orienter leurs passions vers une meilleure compréhension du sens à donner à l’enseignement. Mots clés : recherche axée sur les arts, recherche narrative, approfondissement des connaissances personnelles et professionnelles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".