"I want to enable teachers in their change": Exploring the Role of a Superintendent on Science Curriculum Delivery
Bibliographic record
Abstract
This research inquiry explored the factors influencing successful science program delivery among early- and middle-years schools within a rural school division in central Canada. The study is framed by the author’s personal inquiry into how psycho-social factors at the classroom, school and school division level influence science program delivery. In line with case study methodology, the inquiry uses a variety of qualitative and quantitative methods and data sources to identify the contributors at the classroom, school and divisional level to science delivery. A validated science program delivery evaluation tool, the Science Curriculum Implementation Questionnaire (SCIQ), is used as the foundation for the quantitative data collection and ensuing teacher, administration and science education community discussions. Bronfenbrenner’s bio-ecological model and Rutter’s views on resiliency are used as a framework for interpreting the data collected and understanding the factors supporting successful science delivery. Participants identify a variety of personal attribute and environmental factors and the interplay between these factors as supportive factors contributing to effective science delivery at the classroom, school and divisional level. Implications of this inquiry are discussed, especially within the context of the role of the superintendent in influencing curriculum delivery. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".