Indexes and estimation techniques
Bibliographic record
Abstract
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.
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 | 0.019 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.119 | 0.078 |
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".