Institutional ethnographies on digital technologies: Investigating and developing critical digital literacy practices with high school students
Bibliographic record
Abstract
Recent scholarship on digital technologies underlines a troubling disconnect: teachers and students are increasingly reliant on these tools without considering the ways they influence their lives, their learning, and issues of social and environmental justice. This dissertation explores a research project in which two Canadian Grade 8 Social Studies classes (students aged 13-14) investigated how their everyday experiences online or with digital tools relate to their well-being and the well-being of the planet. The research project provided a context for analyzing how young people are oriented by and through the social relations bound up in digital technologies and texts, while also looking at and enacting ways by which young people can more actively and critically involve themselves in these relations. By facilitating students through an institutional ethnographic inquiry about and with digital tools, this research aimed to reveal how digital tools shape students’ experiences in similar ways, how students understand the critical dimensions of their technological practices, and how pedagogical practices and the structures that mediate students’ learning relate to the development of critical digital literacy practices. In answering these questions, this dissertation highlights some resonances between a participant-centred approach to research and student-centred approaches to education. Unlike existing critical literacy research that focuses mostly on the texts that students engage with, my research broadens the scope of critical digital literacy education to investigate the impacts digital technologies themselves have on students, and the relations between students and the people involved extra-locally in the coordination of students’ experiences with these tools. As the ways that students and educators relate through technologies are traced and discussed, we can work to disrupt – if necessary – or promote – if valuable – the various digital relations we uncover. Supporting the critical use of technology in schools is important for the experiences and educational outcomes of students, as well as for improving the conditions of other implicated parties – both human and ecological
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".