Evaluating the influence of same-sex secondary agriculture classrooms on student career interests
Bibliographic record
Abstract
Single-sex classrooms have been a topic of interest in the educational community since the No Child Left Behind Act of 2001. The academic achievement gap between boys and girls in high school is a complex and multifaceted issue that has garnered significant attention in educational research. Proponents of single-sex classrooms argue that it reduces social anxiety, physical aggression, and can close the achievement gap between boys and girls. This quasi-experimental study compared students in single-sex classrooms and coeducational classrooms of ten various Principles of Agriculture courses in Kentucky, and the influence these teaching models had on students’ career interests. Key findings from this experimental study included: (a) boys in single-sex classrooms ranked their interest in the agriculture, food, and natural resources career pathway the highest; (b) the classroom structure nor the sex of the students were not influential to the gain in students interest in the agriculture, food, and environment careers; (c) boys and girls in single-sex classroom structures were influenced more than their coeducational classroom colleagues in a career interest in education and training, particularly, agricultural education. Recommendations from this study include: (a) exposing boys to careers in agricultural education early in high school; (b) prepare teachers on methodologies that limit gender stereotypes within the agriculture career field; and (c) the need for study replication along with longitudinal and qualitative research.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 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".