Improving Student Achievement: Can Ninth Grade Academies Make A Difference?
Bibliographic record
Abstract
This study focused on student achievement in ninth grade schools or academies compared to ninth grade students enrolled in traditional high schools. Student achievement was measured by standardized test scores. Other variables tested were gender and ethnicity. All students used in this study were enrolled in the ninth grade during the 2005-2006 school year at one of six schools selected for this research. Participants were enrolled in Algebra I and/or Biology I course(s) and therefore took the standardized Subject Area Test in these disciplines. Data indicated students enrolled in ninth grade academies scored significantly higher then ninth graders enrolled in traditional high schools on both the Algebra I and Biology test. Further analysis of data revealed significant differences based on ethnicity in achievement of Biology I students in the ninth grade academies when compared to the Biology I students in the traditional high schools. The African American students in the ninth grade academies had a higher mean score on the Biology I SAPT than Caucasian and African American students enrolled in the traditional high schools. Additionally, the Caucasian students in the ninth grade academies scored only .03 higher than the mean score of African American students in the ninth grade academies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".