Watercolors Awash in Crayoned Responses: Teaching Narrative in Arts-based Praxis
Bibliographic record
Abstract
I work in literacy education, encouraging teacher candidates to experiment with the arts to make a novel come alive for adolescent readers. Part of my research agenda, which is intertwined with my teaching, seeks to make sense of the question: What are the effects of arts-based learning on the teacher candidates’ theoretical and classroom practices? To firstconsider the above research question from my own pedagogical perspective, I draw on my earlier recollections (Adler, 1958) of arts and classroom living using the methodology of narrative inquiry—the study of the ways humans experience the world via the construction and reconstruction of their own stories (Connelly & Clandinin, 1990). Informed by my teaching narrative, crafted in the backdrop of remembered times, I venture forth to address the effects of arts-based learning on the teacher candidates’ theoretical and classroom practices. A more informed construction of our recurring narratives, rekindled by the illumination of early recollections, will play an integral role.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".