MétaCan
Menu
Back to cohort
Record W6976694045 · doi:10.6068/dp14bafc9bc2341

Trend 2000 - 2014. Bureau of Labor Statistics. Current Population Survey: Union Affiliation Statistics: Employed Full-Time | Country: USA | Seasonally Adjusted: Non-Seasonally Adjusted | Demographic Indicator: - Percent of employed, Employed full time, Wage and salary workers, Represented by unions, 2000-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 002-028-002.

2015· other· en· W6976694045 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2015
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCurrent Population SurveyCensusSalaryPopulationEarningsWageSample (material)Population statisticsQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

Bureau of Labor Statistics (2015). Current Population Survey: Union Affiliation Statistics: Employed Full-Time | Country: USA | Seasonally Adjusted: Non-Seasonally Adjusted | Demographic Indicator: - Percent of employed, Employed full time, Wage and salary workers, Represented by unions, 2000-2014. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 002-028-002. Dataset: Reports estimates of the civilian noninstitutional population ages 16 and older that are employed full time. Estimates are presented for all employed persons, members of unions, and workers represented by unions, and segmented by age, race, Hispanic or Latino ethnicity, sex, occupation, industry, and state. Median weekly earnings data are also available for members of unions, workers represented by unions, and non-union workers, and segmented by age, race, Hispanic or Latino ethnicity, sex, occupation, and industry. Full-time workers are those who usually worked 35 hours or more (at all jobs combined). Full-time workers include some individuals who worked less than 35 hours in the reference week for either economic or noneconomic reasons and those temporarily absent from work who usually work at least 35 hours per week. The Current Population Survey (CPS) is a monthly survey of the civilian noninstitutional population ages 16 and older conducted with a probability sample of 60,000 households in the United States by the Census Bureau for the Bureau of Labor Statistics. Presented here are Union Affiliation Labor Force Statistics obtained via the CPS, which provides data for all workers, members of unions, and workers represented by unions, segmented by age, race, Hispanic or Latino ethnicity, sex, occupation, industry, state, and full- or part-time status. Median weekly earnings data are also available for members of unions, workers represented by unions, and non-union workers segmented by age, race, Hispanic or Latino ethnicity, sex, occupation, industry and full- or part-time status. CPS data are collected by personal and telephone interviews. The survey reference period is the calendar week (Sunday through Saturday) that includes the 12th day of the month; the actual survey is conducted during the following week, ie, the week containing the 19th day of the month. Basic labor force data are gathered monthly; data on special topics are gathered in periodic supplements. Persons less than 16 years of age are excluded from the official estimates because child labor laws, compulsory school attendance, and general social custom in the US severely limit the types and amount of work that children under age 16 can do. (Prior to 1948, the sampled population included those ages 14 and older.) Persons on active duty in the US Armed Forces are excluded from coverage, as is the institutional population, which consists of residents of penal and mental institutions and homes for the aged and infirm. The data are annual averages of information collected monthly in the survey, as calculated from not seasonally adjusted monthly data. Category: Population and Income, Labor and Employment Source: Bureau of Labor Statistics The Bureau of Labor Statistics (BLS) of the United States Department of Labor is the principal fact-finding agency for the federal government in the broad field of labor economics and statistics. The BLS is an independent national statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the US Congress, other federal agencies, state and local governments, business, and labor. The BLS also serves as a statistical resource to the Department of Labor. http://www.bls.gov/ Subject: Labor Union Membership, Full-Time Employment, Labor Unions, Full-Time Workers, Civilian Labor Force, Earnings

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)0.002
metaresearch head score (Gemma)0.016
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 score0.157
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.015
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0870.093

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 head0.035
GPT teacher head0.265
Teacher spread0.230 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueData PlanetSame topicLaw, Economics, and Judicial SystemsFrench-language works237,207