MétaCan
Menu
← Back to cohort
Record W6976625149 · doi:10.6068/dp1659f0f0fed2

TREND: Organisation for Economic Co-operation and Development (OECD). Main Economic Indicators (MEI): Labor - Hourly Earnings | Country: Denmark | Indicator ID: LCEAMN01_IXOB, 1971 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-003-007

2018· other· en· W6976625149 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsEconomic statisticsEconomic indicatorIndex (typography)World Development IndicatorsEconomic dataOfficial statisticsNational accountsDescriptive statistics

Abstract

fetched live from OpenAlex

Organisation for Economic Co-operation and Development (OECD). Main Economic Indicators (MEI): Labor - Hourly Earnings | Country: Denmark | Indicator ID: LCEAMN01_IXOB, 1971 - 2017. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 062-003-007 Dataset: This subset of the Main Economic Indicators (MEI) database includes seasonally adjusted average total earnings paid per employed person per hour including overtime pay and regularly recurring cash supplements. These comparative statistics include earnings series from manufacturing and the private economic sector. Data sources are mainly business surveys covering different economic sectors, but in some cases administrative data are also used. Data are expressed in index form (2010=100). The Organisation for Economic Co-operation and Development (OECD) Main Economic Indicators database provides comparative statistics on OECD member countries and other non-member countries. Data availability varies by nation and by time period. See the technical documentation for information on national practices related to the compilation of data. http://www.oecd-ilibrary.org/economics/data/main-economic-indicators_mei-data-en Category: Labor and Employment, International Relations and Trade Subject: Earnings, Wages, Hourly Work 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/

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.113
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0090.033
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0030.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1130.159

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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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 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

Explore more

Same venueData Planet→French-language works237,207→