Bibliographic record
Abstract
This chapter seeks to better understand how austerity governance has been experienced in the eight cities, from the perspective of the local state. As earlier chapters demonstrate, austerity governance is a real challenge for cities and local states, which can often have competing priorities and imperatives. This is because traditionally, local managers and elected politicians are more inclined than those of the upper tiers of the state to listen to and be responsive to the residents of their local constituencies, because they are closer to them. Consequently, the principles and rules in municipalities for managing public budgets are usually more responsive to social demands. However, if the democratic local state is a political unit, with at least some autonomy to enact its values and citizen preferences, it is also subject to a range of structural and contextual constraints. These include cultures and practices of neoliberal marketization and the level of resources available through transfers and taxation. In that respect, local state managers and elected representatives are caught in a difficult situation. On the one hand, they seek to respond to the needs and priorities of their constituents while, on the other, they operate within the constraints set by national priorities of neoliberal marketization and cuts to resources. This leaves them looking two ways, trying to overcome continuous contradictions, conflicts and uncertainties that arise from this difficult positioning. In addition to these immediate and contradictory demands on local officials, questions of local state power are strongly connected with urban culture. This idea can be captured, at least in part, through the concept of ‘ mémoire du lieu ’ (memory of place), as defined by Todd (2017: 468). This ‘ mémoire du lieu ’ frames the way local actors perform and build collaboration, and is an important component of local culture which, in turn, informs popular preferences. Thus, local culture, local preferences and pressures arising from the national and international sources discussed in the previous chapter all combine to set conditions in which local states operate in juxtaposition with market and civil society forces. The compromises reached are therefore partly the result of different strategies of civil society resistance that have proven more or less effective in promoting local autonomy and social solidarity.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".