Current Population Survey, February 1990: Unemployment Benefit Compensation Supplement
Bibliographic record
Abstract
This collection provides data on labor force activity for the week prior to the survey. Comprehensive data are available on the employment status, occupation, and industry of persons 15 years old and over. In addition, unemployed persons were asked a series of supplemental questions about unemployment compensation. The purpose of this supplement was to determine why a growing proportion of the unemployed were not receiving or had not been applying for benefits under the unemployment insurance program. Supplement questions focused on whether respondents had applied for unemployment benefits and whether they had received them since their last job, whether they had received an unemployment check in the week prior to the interview, reasons for not receiving unemployment compensation within the previous week, reasons respondents had not received unemployment compensation since their last job, and reasons for not applying for unemployment compensation. About a quarter of the unemployed respondents were asked the supplemental questions each month. These were respondents 15 years and older who reported either that they did not work in the previous week but had been working before and planned to begin a new job within 30 days, or were laid off, or looking for work. Unemployed persons who were trying to find employment for the first time were not considered eligible for the supplement. Personal characteristics such as age, sex, race, marital status, veteran status, household relationship, educational background, and Spanish origin are also included in the file. (Source: downloaded from ICPSR 7/13/10) Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR03329.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".