The yellow vests and the communicative constitution of a protest movement
Bibliographic record
Abstract
Contemporary protest movements are skeptical of mainstream media outlets, and so to communicate, they make extensive use of social media such as YouTube, Instagram and Twitter. Most research to date has considered how protest movements, as preexistent entities, use such social media to communicate with stakeholders, but little, if any research, has considered how a protest movement is constituted in and through communication. Using the Montreal School’s ventriloquial approach to communication and using YouTube video footage of the gilets jaunes – a contemporary French protest movement – in action, the purpose of this article is to explicate how a protest movement that resists the state’s authority is constituted in and through a textual artifact (a video clip on YouTube). Findings indicate that the protest movement is not only discursively constructed through the commentary that accompanies the video, but it is also constituted by non-human actants such as space, buildings and clothing. The protest movement mobilizes networks of human and non-human actants that invoke a moral authority that resists legally authorized state-sponsored networks which are also made up of human and non-human actants.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.077 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".