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Record W6957873704 · doi:10.6068/dp1645ab1fe7215

TREND: Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Labor - Hours Worked | Country: Italy, Mexico | Socioeconomic Indicator: Average Hours Actually Worked, 2000 - 2012. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-001-045

2018· other· en· W6957873704 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusGainful employmentWork (physics)Socioeconomic developmentPart-time employmentCensus

Abstract

fetched live from OpenAlex

Organisation for Economic Co-operation and Development (OECD). OECD Factbook 2014: Economic, Environmental and Social Statistics: Labor - Hours Worked | Country: Italy, Mexico | Socioeconomic Indicator: Average Hours Actually Worked, 2000 - 2012. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-001-045 Dataset: Presents average hours actually worked per year per person in employment. The average number of hours worked per year is calculated as the total numbers of hours actually worked over the year divided by the average number of people in employment. The data cover employees and self-employed workers, and include both full-time and part-time employment. Employment is generally measured through household labor force surveys. In accordance with the International Labour Organization (ILO) Guidelines, employed persons are defined as those aged 15 years or over who report that they have worked in gainful employment for at least one hour in the previous week or were temporarily absent from work. Estimates of the hours actually worked are based on national labor force surveys in most countries, while others use establishment surveys, administrative records or a combination of sources. Actual hours worked include regular work hours of full-time and part-time workers, over-time (paid and unpaid), hours worked in additional jobs, and time not worked because of public holidays, annual paid leave, illness, maternity and parental leave, strikes and labor disputes, bad weather, economic conditions, and several other minor reasons. This dataset provides indicators included in the OECD Factbook 2014: Economic, Environmental, and Social Statistics, updated annually by the Organisation for Economic Co-operation and Development (OECD). Indicators, reported in 12 broad subject areas, cover a wide range of topics: agriculture, economic production, education, energy, environment, foreign aid, health, industry, information and communications, international trade, labor force, population, taxation, public expenditure, and research and development. Data are provided for all OECD member countries and Brazil, China, India, Indonesia, Russia, and South Africa, where available. NOTE: The data presented here are copyrighted by OECD and reproduction is subject to OECD permissions policies: See http://www.oecd.org/rights for further information. Indicator descriptions are based on the OECD Factbook 2014. http://stats.oecd.org/BrandedView.aspx?oecd_bv_id=factbook-data-en&doi=data-00590-en Category: Labor and Employment, International Relations and Trade Subject: Labor Force Participation, Employment, Work Hours, Adults Source: Organisation for Economic Co-operation and Development (OECD) Established in 1961, when 18 European countries plus the United States and Canada joined together to create an organization dedicated to global development, the Organisation for Economic Co-operation and Development (OECD) today includes 34 member countries from around the globe, ranging from North and South America to Europe and the Asia-Pacific region. Member countries include many of the world’s advanced countries as well as emerging nations. The OECD mission remains the promotion of policies that will improve the economic and social well-being of people around the world. The OECD collects and analyzes data on a broad range of topics to help governments foster prosperity and fight poverty through economic growth and financial stability, at the same time taking the environmental implications of economic and social development into account. The OECD Secretariat collects and analyzes data, after which committees discuss policy regarding this information, the Council makes decisions, and then governments implement recommendations. The performance of individual countries is monitored following implementation via a system of multilateral surveillance and a peer review process. The OECD is headquartered in Paris, France, and it is funded by its member countries. National contributions are based on a formula that takes account of the size of each member's economy. The largest contributor is the United States, which provides nearly 24% of the budget, followed by Japan. http://www.oecd.org/

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How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.182
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3640.182

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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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