Transcription as a dynamic craft in the A day in the Life methodology : Insights into the development of understandings of citizenship in a five-year-old’s transition to school
Bibliographic record
Abstract
In this paper we illustrate and reflect on how the Day in the Life methods enabled us to devise and combine approaches to transcribing and presenting data from a specific day of a five-year-old girl of Indo-Canadian heritage (Gillen & Cameron, 2017) . In the video data we found connections between the multimodal meaning-making practices of Suhani across two encounters in one day, the first in ‘mat time’ at a kindergarten and the second at afternoon tea with her family. In the first the teacher reads aloud to a group, introducing them to the history of beavers as symbols of Canada. Later, at afternoon tea with her grandparents Suhani demonstrates her close attention to the teacher’s multiple modalities while also finding her own ways of bridging gaps in her understandings, drawing on family and media discourses. We explain how we approached this data by drawing on linguistic ethnography (Creese, 2008) enriched by a multimodal approach to studying the co-construction of familial narratives (Cameron and Gillen, 2013). We illustrate our three approaches to transcription used in the study that respond to the suggestion by Copland & Creese, (2015: 196) that transcription should be “fit for purpose” and “provide the level of detail required for the job they have to do”. We conclude by briefly demonstrating the insights that were gained from holding transcription as a dynamic craft.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".