Images, Speech Balloons, and Artful Representation: Comics as Visual Narratives of Early Career Teachers.
Bibliographic record
Abstract
The ways in which teachers adjust to challenges in the process of becoming professionals are complicated. Teacher mentorship, however, is an important step to creating and sustaining a strong professional career. This article discusses new understandings from a Canadian research project: Pedagogical Assemblage: Building and Sustaining Teacher Capacity through Mentoring Programs in British Columbia. Through our use of an a/r/tography informed methodology in teacher mentorship, we have come to understand how the use of comics permits an unfolding of visual narratives as a unique way of contextualizing the complex stories of teaching and learning. Our motivation in employing comics as research outputs is built upon the creation of a product that is reflective of practice and collaboration, and which forms a communicative whole with the broader education community. In this article we provide a macro analysis of the teachers’ sequential narratives by exploring the possibility of merging comics and curricular languages in light of our mentorship comics; then continue with a micro analysis showcasing the collaborative research process of a teacher’s story. We also discuss audience response regarding how comics can be utilized to support and strengthen teachers’ professional growth. We aim to provoke new possibilities of comics through our research in teacher mentorship, as well as create new spaces for arts-based educational research in a broader educational arena.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".