A Logistic Regression Model Comparing Astronomy And Non-Astronomy Teachers In Québec’s Elementary Schools
Bibliographic record
Abstract
Based on the results of an online survey of 500 Québec’s elementary (K-6) teachers conducted in 2015 that probed the way respondents teach astronomy to their classrooms, their background in S&T, their pre-service education, their aims and goals for astronomy teaching, their attitude toward teaching astronomy, the resources and materials they use, their view on the effectiveness of pre- and in-service training, and their need for in-service training, we present a logistic regression model comparing elementary teachers in our survey that teach astronomy to their class (“Astronomy” teachers, N = 244) and those who don’t (“Non-astronomy” teachers, N = 256), to reveal factors that seem to facilitate or hinder astronomy teaching in Québec’s elementary classrooms. Based on the model, several ways to enhance the teaching of astronomy in Québec’s K-6 classrooms are proposed: offer high-quality pre- and in-service training in astronomy to elementary teachers, raise the profile of science teaching in elementary schools, and help teachers realize the importance of teaching astronomy in their classrooms to cover the curriculum standards.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".