Bibliographic record
Abstract
In a highly mediated world, understanding how we communicate becomes an essential skill of citizenship. In Canadian secondary schools this is often taught as media literacy, either as a new stand-alone subject or integrated within the existing curriculum. As a school subject media literacy is particularly difficult to define and document because it continually changes to keep pace with technological innovation. Combining aspects of genealogy and scene-based analysis helps attend to the spatial and temporal formations of media literacy, thus bringing principles of circulation, exchange, and ephemerality into view. The first part of this dissertation considers the construction of media literacy as a secondary school subject in Canada. I problematize the dominance of a single historical narrative, where Ontario has come to stand in for the rest of the country, by tracing that narrative’s genealogical threads any by considering how it has travelled both nationally and internationally. I argue that a multitude of narratives have been obscured by this singular version of events. In the second part of the dissertation I study one such alternative narrative in British Columbia, using the sensitizing concept of “scene” to better understand how school subjects are produced. The various places, actors, organizations and activities producing a media literacy scene in BC do not neatly fit into the dominant national narrative. I discuss some of the ways in which school subjects have been studies, and then advance a model of scene-based analysis as a more flexible, generative research framework for studying social phenomena. I argue that media literacy is more than just a collection of canonical theories and methods. Its specific formation is contingent upon and shaped by forces both inside and outside of institutional learning, influenced by national and transnational trends, as well as locally specific conditions and relations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".