Bibliographic record
Abstract
This is the time for a critical digital pedagogy that simultaneously recognizes the potential inherent in social media to challenge power and build movements alongside the dangers lurking in a fake news era that spreads hate, division and distraction. This paper explores how Canadian digital feminist activists challenged conservative power over three federal elections with innovative creativity using what I have termed, critical pedagogical humour, resulting in a spontaneous online social movement that helped oust the Prime Minister. Using a social-justice qualitative mixed-methods approach, this study informs online political practice and pedagogy drawing three conclusions: 1) Social media makes responsive activism possible, lifting most barriers, and enabling risk-taking; 2) Social justice struggles rely on informal education based on truth-telling, rooted in values and deliberately using humour for its innocuous delivery that disarms and opens doors and; 3) Social media provides unique opportunities to respond to events in real-time while creating a historical record documenting government activity and resistance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".