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Record W6976497272 · doi:10.6068/dp160858201353

Trend 07/2007 - 07/2010. Bureau of Labor Statistics. National Compensation Survey [Archive]: Civilian Workers - Hourly Mean Earnings | Country: USA | Labor Metric: All workers | Industry: All workers | Occupation: Marriage and family therapists, 07/2007-07/2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 002-023-003.

2017· other· en· W6976497272 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsMetropolitan areaWages and salariesSample (material)IncentiveCompensation of employeesPaymentWorkers' compensationQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

Bureau of Labor Statistics (2017). National Compensation Survey [Archive]: Civilian Workers - Hourly Mean Earnings | Country: USA | Labor Metric: All workers | Industry: All workers | Occupation: Marriage and family therapists, 07/2007-07/2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 002-023-003. Dataset: Presents estimates of hourly mean earnings of civilian workers in the United States, in total and for select metropolitan and nonmetropolitan areas. Estimates are segmented by union vs nonunion, full- vs part-time, and time vs incentive status of workers, and by industry and occupation. The civilian sector, by survey definition, excludes federal government, agricultural, and household workers. Reports data from the National Compensation Survey (NCS) on occupational earnings of workers in the United States, 2007-2010. Statistics are provided for select metropolitan and nonmetropolitan areas, and the nation. The estimates originate from the NCS Locality Pay Survey data, weighted to represent the nation as a whole, and include pay for workers in major sectors (civilian, private, state and local government) and by various occupational and establishment characteristics. Earnings are defined as regular payments from the employer to the employee as compensation for straight-time hourly or salaried work. The data are collected by BLS field economists on a probability sample of establishments selected using a 3-stage stratified design. The NCS sample is classified by the 2007 North American Industry Classification System (NAICS) and occupations are classified using the 2000 Standard Occupational Classification (SOC) system. With the enactment of the federal government's 2011 budget, the Locality Pay Survey (LPS) portion of the National Compensation Survey (NCS) was eliminated. The Bureau of Labor Statistics now provides data on wage data by occupations using data from the Occupational Employment Statistics and NCS programs. 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: Civilian Personnel, Civilian Labor Force, Civilian Employment, Earnings, Wages, Hourly Work

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 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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, 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 score0.520
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0060.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0260.012

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 head0.079
GPT teacher head0.333
Teacher spread0.254 · 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
Published2017
Admission routes1
Has abstractyes

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