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Record W7072097618

Why Nottingham and Birmingham will be followed by more cities running out of money

2023· article· en· W7072097618 on OpenAlexaboutno aff

Bibliographic record

VenueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Services Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsStatutory lawGovernment (linguistics)Cover (algebra)Quarter (Canadian coin)Section (typography)Private sectorValue for money
DOInot available

Abstract

fetched live from OpenAlex

Almost a fifth of England’s council bosses have warned they are running out of money so badly that they will become effectively bankrupt this year or next. Some councils have already run out of funds. Nottingham and Birmingham councils were the latest to issue what are known as “section 114 notices”. These notices mean a council’s expected income is not enough to cover what it plans to spend – and that it cannot find a solution by itself. \n \nAnd while councils cannot actually go bankrupt in the same way that a person or private business can, the act of issuing a section 114 is a very serious issue. It means all spending, other than providing statutory services such as adult and children’s social services, is immediately suspended. This means no money for new contracts or projects on transport, waste, planning, leisure or culture. \n \nAs a result, the council is placed into a relationship with the government which is something more akin to a company going into administration. The organisation is effectively led and managed by commissioners appointed by the government.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.286
Threshold uncertainty score0.569

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0140.007
Scholarly communication0.0150.010
Open science0.0020.004
Research integrity0.0110.008
Insufficient payload (model declined to judge)0.1650.042

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.

Opus teacher head0.035
GPT teacher head0.328
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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