Aesthetic Archives: Pre-Service Teachers Symbolizing Experiences through Digital Storytelling
Bibliographic record
Abstract
With the novelty of digital storytelling, there is increased enthusiasm in taking up forms of filmmaking in teacher education as a way to promote self-directed reflective practices. However, the visual quality of digital stories holds blind spots, in other words, what we defend against seeing in these approaches, as well as their implications for meaning making. With this in mind, the authors undertook a multi-year process that involved having pre-service teachers work with the aesthetic medium of digital storytelling to engage with critical incidents in their teaching. In this context, digital storytelling works as an aesthetic archive that symbolizes the conflicts of learning to teach. This article focuses on a close reading of two cases which provide insight into the ways in which future teachers use digital storytelling to both wrestle with and question the significance of the identity-making venture of becoming teachers. Additionally, the authors consider the personal and social implications of such an undertaking for teacher-educators.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".