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Record W2122460975 · doi:10.1080/0042098042000316155

Old Industrial Regions and Employability

2005· article· en· W2122460975 on OpenAlexaboutno aff
Mike Danson

Bibliographic record

VenueUrban Studies · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsEmployabilityMetropolitan areaRestructuringLabour economicsGovernment (linguistics)Economic growthHuman capitalQuarter (Canadian coin)UnderemploymentWork (physics)BusinessEconomic restructuringAccreditationUnemploymentDevelopment economicsEconomicsGeographyEngineeringFinance

Abstract

fetched live from OpenAlex

Inactivity has been growing across the developed world and is especially high in old industrial areas. A general move towards more flexible labour markets and the restructuring in these regions over the past quarter of a century have led to a change in the supply and demand conditions for employment. There is an increasing dependence on school and higher education qualifications and associated transferable skills and competencies, while the decline of traditional occupations has left many without jobs and facing multiple barriers to regaining employment. Often lacking demonstrable and accredited human capital and work experience, individuals with such employability problems have been concentrated in particular households and communities-polarising society. Policy interventions are required to address these obstacles and social exclusion, but central government appears reluctant to face the full direct costs of implementation. More radical innovative solutions are now being proposed at 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 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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.156
GPT teacher head0.395
Teacher spread0.239 · 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

Citations34
Published2005
Admission routes1
Has abstractyes

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