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Record W4256626740 · doi:10.1787/reg_glance-2016-50-en

Indexes and estimation techniques

2016· book-chapter· en· W4256626740 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOECD regions at a glance · 2016
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicUnemployment and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEstimationComputer scienceEconomics

Abstract

fetched live from OpenAlex

Forpolicymakersandcitizensalike,thinkinggloballyincreasinglyrequireslookinghard at the many different local realities within and across countries.A thorough assessment of whether life is getting better requires a wide range of measures that are able to show not only what conditions people experience, but where they experience them.OECD data show remarkably high disparities in people's living conditions across regions and cities: for example, there is a 20 percentage point difference among unemployment rates between regions within Italy, Spain and Turkey, comparable to the difference between the national unemployment rate of Greece and that of Norway.And life expectancy varies by 8 years among all OECD countries, but by 11 years across Canada's provinces and by 6 years among states in Australia and in the United States.This report provides a comprehensive picture of the level of progress in OECD regions and metropolitan areas towards more inclusive and sustainable development.It does so through eleven well-being dimensions, those that shape people's material conditions (income, jobs and housing) and their quality of life (health, education, access to services, environment, safety, civic engagement and governance, community, and life satisfaction).These dimensions are gauged through outcomes indicators, which capture improvements in people's lives.The report also looks at what local resources are being mobilised to increase national prosperity and well-being, to better assess the contribution of regions to national performance.Since the economic crisis of 2008, many regions are still struggling to increase the productivity of firms and people and to restore employment.Traditionally, relatively few regions have led national job creation: on average, regions that concentrated 20% of OECD employment in 2000 have created one-third of the overall employment growth in the period 2000-14 and 50% or more in the Czech Republic, Estonia, Hungary, Korea and Poland.However, since 2008 employment growth has also slowed down in the most dynamic regions in all OECD countries, with the exception of Israel, Luxembourg, Mexico and Turkey.Regional and local governments (collectively known as "subnational governments" or SNGs) control many policy levers for promoting prosperity and well-being.SNGs were responsible for around 40% of total public expenditure and 60% of public investment in 2014 in the OECD area.Education, health, general public services, economic affairs and social expenditure represent the bulk of SNG expenditure (85%).At the same time, responsibilities for these sectors are often shared, requiring co-ordination across national and subnational levels of governments to ensure effective and coherent policy making.Indeed, lack of such co-ordination was indicated as a top challenge by three-quarters of European SNGs participating in an OECD-Committee of the Regions survey in 2015.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.028
GPT teacher head0.199
Teacher spread0.171 · 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