The power of slogans: using protest writings in social movement research
Bibliographic record
Abstract
This article explores the theoretical and empirical interest of protest slogans and writings for social movement research. It shows how this material can contribute to a better understanding of collective identities, emotions, and claims made in contemporary demonstrations. Theoretically, it invites researchers to go beyond the actual ‘words’ to invest these slogans as political performances: increasingly individualized and diversified, these writings carry a public staging and a political discourse that can address multiple audiences. Therefore, they give access to the individual and collective voices expressed within contemporary social movements – and the way they interplay. To take into account their diversity, the article proposes a typology of the main political functions of these writings – whether they aim to lay claims, to proclaim, to mobilize or to witness, which will determine their very form and their favored support. Secondly, the article revisits the ethical and methodological issues raised by the collection and analysis of protest writings for the empirical study of social movements. It examines different ways of mining their potential for comparison and mixed method device, through either textual, visual or qualitative analysis. To do so, it draws on the experience and results of an international study of youth social movements in the second decade of the twenty-first century, to which this method was widely applied to identify and compare the fundamental rhetorics of these post-2008 protests.
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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 | 0.028 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".