RANKING: United States Census Bureau. Quarterly Workforce Indicators (QWI): Full-Quarter Employment (Stable) | Industry Group: Fiber, Yarn, and Thread Mills (3131) | Gender: All Genders | Age Group: All Ages (14-99) | Race: All Races | Ethnicity: All Ethnicities | Education Level: All Education Categories | Firm Age: All Firm Ages | Firm Size: 500+ Employees | Owner: All Private, 2016/3 - 2017/3. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 001-064-003
Bibliographic record
Abstract
datasets.shared.infosheet.CitationMgr@30d Dataset: Provides estimates of stable jobs, ie, the number of jobs that are held on both the first and last day of the quarter with the same employer. This is often, but not necessarily, the same as being employed for a full quarter (eg, an on-call substitute teacher may have earnings in each of three consecutive quarters, but intermittently). " The Quarterly Workforce Indicators (QWI) provide local labor market statistics by industry, worker demographics, and firm age and and size. Because the QWI source data are unique job-level data that link workers to their employers labor market data in the QWI is available by worker age, sex, educational attainment, and race/ethnicity, which allows for analysis by demographics of a particular local labor market or industry – for instance, identifying industries with aging work forces. Links between workers and firms also allow the QWI to identify worker flows – hires, separations, and turnover – as well as net employment growth. As most hiring activity is the consequence of worker turnover rather than employment growth, a focus on employment growth alone may misrepresent employment opportunity in the local labor market. Wages by industry and demographics as well as by whether the worker was newly hired are also available, allowing comparison of wages between new hires and continuing workers. Also, wage growth for similar workers across industries can be compared to identify important local labor market trends. The source data for the QWI is the Longitudinal Employer-Household Dynamics (LEHD) linked employer-employee microdata. The LEHD data is a massive longitudinal database covering over 95% of US private sector jobs. Much of this data is collected via a unique federal-state data sharing collaboration, the Local Employment Dynamics (LED) partnership, which is a cooperative venture between the US Census Bureau and state agencies of all 50 states, the Washington, DC, Puerto Rico, and the US Virgin Islands. Through the LED program, partner states voluntarily submit quarterly data files from existing administrative record systems, which are combined with a range of other data sources to generate public use products, such as QWI. By integrating data used to administer public programs with existing census and surveys, a new national jobs database is generated at very low cost and with no additional respondent burden. https://www.census.gov/data/developers/data-sets/qwi.html Category: Labor and Employment Subject: Employment, Jobs Source: United States Census Bureau The US Census Bureau is a bureau of the US Department of Commerce. The major functions of the Census Bureau are authorized by Article 2, Section 2 of the United States Constitution, which provides that a census of population shall be taken every 10 years, and by Title 13 and Title 26 of the United States Code of Federal Regulations. The Census Bureau is responsible for numerous statistical programs, including census and surveys of households, governments, manufacturing and industries, and for US foreign trade statistics. The first US census was conducted in 1790 for the purposes of apportioning state representation in the US House of Representatives and for the apportionment of taxes. https://www.census.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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.083 |
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".