Session 40: Understanding Policy Narratives Shortcomings and Difficulties in Transcoding Public Policy in Metropolitan Areas
Bibliographic record
Abstract
Abstract. How does the symbolic violence of political language find expression in the process of metropolization and what dominant rhetorical forms does it take? We have drawn on the results of recent studies of public policy in the urban regions of Naples, Toronto, Montreal, Lyon, Strasburg and Grenoble to call into question the nature of the political narratives used in the metropolitan development of these major urban regions. We draw the conclusion from these studies that the political élites involved (elected representatives, administrative officials and associated experts) are experiencing trouble in finding a credible and legitimate form of discourse at the inter-‐communal level when they try to formulate and justify their priorities for public action at this governmental level in matters concerning territorial planning and development and the promotion of social cohesion. In their different ways of promoting the metropolitan area, one observes a serious failure to convey meaning in their use of political rhetoric devoid of emotion, as also in their professional explanations which fail to galvanize and captivate the public because of their lack of expressive eloquence. To understand how these failings in political narrative illustrate but also explain the ineffective, uncertain and tentative governability of urban institutions, this paper sets out to integrate into a cognitive approach to public policy the effects of context and of territoriality, by calling upon different analytical traditions (includind Cultural Studies and Narrative Policy Analysis). This combinative approach leads us to emphasize in conclusion that the transcoding processes of conveying the local common good can seriously hinder the emergence of a working political order at the metropolitan level. 1 Introduction: Language
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".