Making New Political Spaces: Mobilizing Spatial Imaginaries, Instrumentalizing Spatial Practices, and Strategically Using Spatial Tools
Bibliographic record
Abstract
This paper explores space as the object of mobilization (rather than focusing on space as resource or constraint, or on the spatial configuration of actors within the organizational structure of a movement). In the context of state-restructuring processes, it is argued that new political spaces result not only from social movement activities (as in the drive for ‘free spaces’), but also in a dynamic interaction between state and civil society actors. The author asks what it takes to create a new, effective, and significant political space. Three elements are explored empirically and theoretically: the production of allegiance and legitimacy through spatial imaginaries, the instrumentalization of spatial practices and of the political culture, and the strategic use of spatial tools. In light of the case of Toronto, where a new regional political space eased the normalization of neoliberalism, it is concluded that new political spaces create the conditions for political exchange, but do not guarantee emancipation, democracy, and justice. Overall, the author's aim is to discuss the concept of political space and the analytical advantages provided by its openness to fluidity, uncertainties, uninstitutionalized interactions, and various forms of rationalities (imaginaries, everyday practices, as well as strategic calculation) in the state-restructuring and rescaling debate.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 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".