Charting new directions: The Potential of Actor-Network Theory for Analyzing Children’s Videomaking
Bibliographic record
Abstract
This paper represents preliminary efforts to understand what Actor-Network Theory (ANT) might contribute to our interest in analyzing what we hope are enhanced educational practices for second language (L2) learners. This theory encourages us to examine more closely the things, the tools, the non-human actants that are active in particular educational practices, and how those tools and not others, “exclude, invite and regulate particular forms of participation” (Fenwick and Edwards, 2010, p. 7). We identify aspects of ANT that are relevant to our work on videomaking, describe our videomaking research and provide two illustrations of how we began to see what ANT might offer in analysis of our video data and to consider its potential for guiding our ongoing fieldwork. We argue here that ANT highlights the importance of paying attention to the production of networks between both human and non-human actors during the videomaking process to understand how these interactions shape the school experiences of language learners.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".