Bibliographic record
Abstract
Humour and satire have figured only marginally in the recent groundswell of activist literature dedicated to the renewal of tools, tactics, and strategies. In an effort to evaluate the effectiveness and appropriateness of humour and media hoaxing as tactics within activist communities—in a moment characterized by increased distrust in news organizations and information—this essay offers insight into artist and activist thinking on a topic rarely discussed in social justice activism circles. In drawing on the views and perspectives of individuals that participated in a weekend-long workshop held in Halifax, Nova Scotia, and run by the Yes Lab for Creative Activism, I argue that humour and media hoaxing are under-utilized tactics and approaches that nevertheless inspire a great deal of discussion and reflection, and retain a distinctly positive charge in their future application to social justice struggle. More specifically, interviews culled from this one-time event point to the challenges and opportunities of integrating media strategies, humour, and hoaxing, all the while acknowledging the defining tensions and asymmetries that mark the current moment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".