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Record W2994257352

EMPLOYMENT BY SECTOR IN THE EUROPEAN UNION, THE UNITED STATES, MEXICO AND CANADA, 1985-2005

2006· article· en· W2994257352 on OpenAlexaboutno aff
María del Carmen Guisán Seijas, Eva Aguayo Lorenzo

Bibliographic record

VenueEERS. Estudios económicos regionales y sectoriales · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicUnemployment and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionWageMember statesEu countriesEconomicsDemographic economicsGeographyBusinessInternational tradeLabour economics
DOInot available

Abstract

fetched live from OpenAlex

We present a comparison of the rates of employment by sector in 15 European countries, and 3 Northern American countries. We find that EU15 average rate of employment in Services and total is below the United States and Canada, and relate this fact with the level of industrial development and other factors. The main conclusion is that although industrial development is the main cause of relatively low levels of real value-added and employment rates in countries such as Spain, Mexico, Greece and Portugal, there are other EU countries with higher level of industrial development, which show a rate of employment in services below their capacity, such as in the cases of France and Germany, which may be due to rigidities in taxes and rules. The most outstanding EU countries reaching both a high rate of employment and average wage above EU mean are the United Kingdom and 7 smaller countries: Austria, Denmark, Finland, Ireland, Luxembourg, Netherlands, and Sweden

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)0.000
metaresearch head score (Gemma)0.001
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 score0.100
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.180
Teacher spread0.161 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEERS. Estudios económicos regionales y sectorialesSame topicUnemployment and Economic GrowthFrench-language works237,207