Building Critical Digital Literacies for Social Media through Educational Development
Bibliographic record
Abstract
Conceptions of critical digital pedagogy extend the tenets of critical pedagogy for the study and use of digital technologies. Engaging with foundations of critical pedagogy as they apply to digital spaces, including social media, the purpose of this article is to explore how critical digital literacies can inform and be enacted in educators’ learning and development, with a focus on post-secondary contexts. Through an analysis of GIFs and memes that are frequently shared on social media, the author considers potential entryways for building critical digital literacies in teaching and learning. Using a critically reflective approach, the author makes connections between recent scholarship in these areas, as well as her own research of digital literacies, and examples from educational development practice that aim to promote criticality in action.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| 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".