TREND: United States Census Bureau, Bureau of Labor Statistics. Consumer Expenditures: Operators, Fabricators and Laborers | Category: Alcoholic beverages | Socioeconomic Indicator*: Alcoholic beverages, 1984 - 2015. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 002-008-078
Bibliographic record
Abstract
United States Census Bureau, Bureau of Labor Statistics. Consumer Expenditures: Operators, Fabricators and Laborers | Category: Alcoholic beverages | Socioeconomic Indicator*: Alcoholic beverages, 1984 - 2015. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 002-008-078 Dataset: Refers to the occupation in which the reference person received the most earnings during the survey period. The occupational categories follow those of the Census of Population. Operators, fabricators, and laborers includes machine operators and assemblers, transportation workers, handlers and laborers, and farming, forestry, and fishery workers. The Consumer Expenditure Survey is a nationwide survey conducted annually for the Bureau of Labor Statistics (BLS) by the United States Census Bureau. The consumer expenditures program consists of two surveys--the Quarterly Interview Survey and the Diary Survey--that provide information on the characteristics, expenditures, and income of American consumers. For the diary survey, respondents complete a diary of expenses for two consecutive 1-week periods. The diary survey is designed to obtain data on frequently purchased items, such as food or housekeeping supplies, that respondents are less likely to recall over time. For the interview survey, respondents report data to an interviewer. Each sample household is interviewed once per quarter, for five consecutive quarters. This survey is designed to collect data on major items of expense, such as property purchases or vehicle purchases, and those that occur on a regular basis, such as rent or utility payments, that respondents recall for 3 months or longer. Each year approximately 30,000 persons participate in the Quarterly Interview Survey, and 15,000 participate in the Diary Survey. The dataset presents annual income and expenditures integrated from the Interview and Diary surveys in varying detail, classified by income, age, consumer unit size, and other demographic characteristics of consumer units. http://download.bls.gov/pub/time.series/cx/ Expenditures consist of the transaction costs, including excise and sales taxes, of goods and services acquired during the interview or recordkeeping period. Detailed inclusions and exclusions for expenditure categories are available on the BLS web site. Category: Labor and Employment, Prices, Consumption, and Cost of Living Subject: Unskilled Workers, Occupations, Household Income, Labor, Wages, Earnings, Salaries, Consumer Spending 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/
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.087 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".