Unionization rates by Commuting Zone in the United States
Bibliographic record
Abstract
This is a computation of predicted unionization rates by 1990 Commuting Zones in the United States. The predictions are for the year 2000.<br><br> Issue: union status is available in the Current Population Survey (Outgoing Rotation Groups), but the CPS does not have county identifiers for the whole population, which are necessary to assign the CZ a CPS respondent lives in. Moreover, even with access to restricted CPS data files, the CPS is meant to be representative at the state level, so estimates at the CZ level may not be accurate. <br><br> Solution: use the CPS Merged Outgoing Rotation Groups (CPS MORG) from 2000 to estimate the probability of being unionized by state, sex, age group and 3-digit industry. Then, use the Quarterly Workforce Indicators (QWI) to get employment figures by county, sex, age group and 3-digit industry. Impute the estimated probability from the CPS MORG at the state, sex, age group and 3-digit industry. Assign each county to its CZ. Then compute weighted average of probability of being unionized by CZ, where employment counts are used as weights. <br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".