Les métropoles à l'épreuve de leur récit politique: Le transcodage contrarié des émotions et de la technique
Bibliographic record
Abstract
How expresses the symbolic violence of language policy (and what rhetorical dominant) in the process of metropolisation? We mobilized the results of recent surveys conducted on public policies in urban areas of Naples, Toronto, Montreal, Lyon, Strasbourg and Grenoble to reflect on what resembles a testing time of the metropolitan narrative. We indeed the fact that political elites (elected officials, experts) are struggling to produce audible speech and legitimate intercommunal level when trying to set priorities for public action on this scale in terms of planning, development and social cohesion. In the ways of telling cities, there is both a sense of failure (of political rhetoric without emotion) and a power of oratory (speeches without professional seduction). To understand how these political narratives illustrate deficits but also explain the governance uncertain, incomplete and differentiated urban institutions, the paper proposes to incorporate into the cognitive effects of public policy context and territoriality in mobilizing different analytical traditions (Cultural Studies, the entry with the symbolic politics, Narrative policy Analysis). This combination can insist, in conclusion, the transcoding process of the common good local thwart the emergence of a political order in the metropolitan level.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".