Making Sense of Video Instruction: An Ethnomethodological Analysis of Following a YouTube Croissant Making Tutorial
Bibliographic record
Abstract
With the use of video instruction becoming more prevalent, this thesis looks at the methods learners use to navigate video tutorials through an ethnomethodological lens. As ethnomethodology is concerned with the way members of society, together, make sense of everyday situations, the way users make sense of video instruction, compared to other mediums of instruction, is an important ethnomethodological question. Using auto-ethnographic video recordings and multi-modal transcription methods, this thesis looks at an instance of a learner using a video tutorial to learn how to make croissants by hand. The auto-ethnographic methods used in this project are designed to attempt to mitigate issues of bias and representation often associated with this form of research, by using various iterations of participant-observation tools. As well, to ethnomethodologically examine the data captured, a multi-modal transcription scheme has been devised, using aspects of established schemes, but with features that are unique to this project. Many of the tasks completed by the learner involve methods of measurement that are either numerical and involve the use of scales or embodied, involving the culturally skilled human body. Acknowledging embodied forms of measurement more comprehensively will benefit studies of video-mediated instructions as well as the production of such instructions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".