Comparing collective actions beyond national contexts : ‘local spaces of protest’ and the added value of critical geography
Bibliographic record
Abstract
Comparative research has been rare in social movement studies and scholars who embark on this path are confronted with several analytical and methodological challenges. This paper offers an avenue for developing comparative analytics using ‘local spaces of protest’ as path for comparison. We first situate this concept vis-à-vis other models available (field, space, arena, networks). We then define the three dimensions of ‘spaces of protest’: specific aim prompting collective action; particular distribution of political actions along different poles (electoral, protest, concerted and participation) and specific scales of protest (local, societal or/and global/glocal). By invoking critical geography to enhance the definition of place and scales of actions, we propose a systematic set-up for comparison. The last part of the paper shows how this notion allows for the comparison of local social forums (gatherings of activists fighting against neoliberal globalization) in two societies, France and Québec. The data presented have been gathered during extensive fieldwork and semi-structured interviews conducted between 2010 and 2011.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".