Updating the Texas Cost of Education Index
Bibliographic record
Abstract
Common sense suggests that some school districts must pay more to hire good teachers than other school districts. For example, districts in big cities must pay more than other districts to compensate for the higher cost of living, and districts with large numbers of students who have limited English proficiency must pay more to compensate for the more challenging teaching environment. Texas is one of the few states that explicitly recognize this sort of cost differential in their school finance formulas. The adjustment that Texas currently uses to compensate school districts for cost differentials is the Cost-of-Education Index (CEI). The CEI is an index factor that reflects the geographic variation in costs of education due to factors deemed beyond the control of the school district. The Texas school finance formula uses the CEI to adjust each district's basic allotment and weighted average daily attendance. Under current law, the Texas CEI affects the distribution of approximately $1.23 billion in state aid to school districts each year. The existing CEI has not been updated since its adoption in 1990, however. As a consequence, approximately 13 percent of state aid to school districts is distributed on the basis of a ten-yearold analysis of cost differentials. Therefore, the Texas legislature directed the Charles A. Dana Center at the University of Texas to conduct a study of variations in known resource costs and costs of education beyond the control of a school district. The Dana Center
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".