Bibliographic record
Abstract
How has the recession impacted on firms, people and places? How have local and regional authorities responded? This book aims to answer these questions by offering an overview of the impacts of the recession on people and places and how it has affected local authorities in the UK and other OECD countries. Being ‘close to the ground’, local authorities are usually at the forefront of dealing with the impacts of recession on people and places. During recessions, they face important challenges: on the one hand they have to cope with increasing demand for services and on the other hand they may face a decrease in their income due to the slowdown in the economy. And with the shift from local government to local governance in the last 10 years, they also have an increasing role in terms of coordinating various organisations in the delivery of local services.\nThis book begins by looking at the potential impacts of downturns and economic shocks on firms, workers, communities and places, both in the short and long term (Part I). Part II then looks at interventions and responses that local authorities can put in place on their own or in partnership with other local, regional and/or national actors to try to deal with these differential impacts. Building on these insights, part III offers international perspectives, outlining the role of local authorities during the recession in France, Canada and Australia.\n[book abstract - an abstract for this individual chapter is currently unavailable].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".