Educational Equity Patterns in South Carolina Career and Technical Education
Bibliographic record
Abstract
Fuller Hamilton et al. (2015) review provided a suggested model to improve Career and Technical Education (CTE) equity so that this study could be replicated systematically. National resources examining CTE educational equity components did not exist. The problem addressed in the replication study was the need to explore educational inequity within the South Carolina CTE Health Science career cluster. No CTE educational equity research exists in South Carolina, so the purpose of the replication study was to explore educational inequity within the South Carolina CTE Health Science career cluster. Cultural Replication Theory was the conceptual framework used for this replication study. Four research questions were formulated to examine the CTE enrollment patterns in South Carolina concerning four demographic characteristics, namely sex, race/ethnicity, region, and socioeconomic status. Students enrolled in CTE within South Carolina during the 2018-19 school year was the population selected. Secondary data was collected from a sample of 196,318 CTE enrollees and examined using descriptive analysis procedures. Overall results were not uniform. Inconsistent levels of inequity existed within race, ethnicity, and sex. In addition, inequity was present regarding regional effects and socioeconomic status. Future recommendations for research include conducting a qualitative or mixed-method study to further explain the enrollment patterns of CTE programs in South Carolina. Implications for practice to address the inequities in South Carolina include improving the underrepresentation of educators by sex and race/ethnicity, recommending equity audits, examination of access and availability of opportunities within CTE programs, and encouragement of all educators actively adopting and advancing an equity agenda from the original study.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".