LANDSCAPES OF MEANING: FROM CHILDHOOD ART TO GEOGRAPHIES OF SELF AS ARTIST/RESEARCHER/TEACHER
Bibliographic record
Abstract
This visual journey, which revisits childhood art as an entry point to inquiry centred on my landscapes of meaning as an educator, represents a self portrait about identity and place that is told from the multiple subjective geographies of self. My collection of childhood artwork offers a different lens to understand historical conditioning and socially constructed perspectives. Although these works reflect common motifs, the thematic trends warrant further consideration, including themes that contribute to ways of being as an artist, researcher, and teacher today. Keywords: childhood art, arts‐based research, feminist research, identity and place, curriculum development Ce périple visuel, qui revisite des créations artistiques de l’enfance comme point de départ d’une recherche centrée sur les paysages d’une enseignante, constitue un autoportrait axé sur l’identité et le lieu à partir de plusieurs géographies du moi. Cette collection d’œuvres remontant à mon enfance offre un prisme différent pour comprendre le conditionnement historique et les points de vue structurés par la société. Bien que ces œuvres reflètent des motifs courants, certains axes thématiques méritent de faire l’objet d’une analyse plus fouillée, notamment ceux qui contribuent aux façons d’être de l’auteure en tant qu’artiste, chercheuse et enseignante aujourd’hui. Mots clés : créations artistiques de l’enfance, recherche axée sur les arts
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.052 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".