Bibliographic record
Abstract
the Annual Meetings of the Canadian Economics Association, for comments on earlier versions of this research. We thank Adrienne ten Cate, Sule Korkmaz, and Stéphanie Lluis for research assistance and the SSHRC and CILN for research funding. The Dynamics of US Labor Force Attachment We analyze the dynamics of labor force attachment in the US by studying patterns of transition behavior for individuals matched month-to-month using data from the new Current Population Survey. Specifically, we examine transition behavior for four labor market states: employment, unemployment, marginal attachment (“wanting work ” but not searching), and non-attachment (“neither searching nor wanting work”). Our methods test whether various degrees of attachment among the non-employed are behaviorally distinct and illuminate the nature of dynamics among a broader set of labor market states than is usually examined. Results from the unconditional transition rates over time suggest that the breakdown of the non-employed into three categories is a useful approach that is supported by the data. These results are confirmed and enhanced by estimation of a number of multinomial models of labor market dynamics, and by estimation and testing within a duration modeling framework that allows for dependence. Moreover, these findings are consistent with earlier results found for longer time-periods using Canadian data, although the present work adds significantly to these results by showing that neither seasonality nor duration dependence issues confound this evidence.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".