Scaling up community college baccalaureates in Washington State: Labor market outcomes and equity implications for higher education
Bibliographic record
Abstract
Community and technical colleges in Washington state were early adopters in the growing trend to offer bachelor’s degrees, actively expanding these degrees over the last 15 years. This study describes the evolving state policy landscape on community college baccalaureate (CCB) degrees in Washington in certain programs previously classified as terminal career-technical education and assesses labor market outcomes for graduates of three high-demand program areas conferring these degrees. Comparing bachelor’s graduates of community colleges to regional university graduates, CCB graduates demonstrated slightly higher employment and earnings in the first quarter post-graduation. However, university graduates caught up to approximately the same or slightly higher earnings as CCB graduates by three years post-graduation. Differences in age and prior work experience of graduates in the two groups may help explain these findings but variation in employment and earnings by gender and race were persistent for both groups, with pronounced disparities for female and some racially minoritized graduates. These findings can inform state policy on baccalaureate attainment, CCB degrees as well as university bachelor’s degrees, to help address inequities in higher education. Future studies evaluating the effects of college degrees on employment and earnings may also be enriched by these results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".