The development, validation and application of a science curriculum delivery evaluation questionnaire for indigenous māori settings
Bibliographic record
Abstract
The study described in this paper examines the procedures used in the identification of the broad and complex factors influencing science curriculum delivery in predominantly Māori settings where the teaching of science, in particular Pūtaiao i Roto i te Marautanga o Aotearoa, is the responsibility of non-specialist science teachers and the teaching of science advocates an orientation to contemporary science in the context of Te Ao Māori, an indigenous epistemology. Furthermore, it describes the processes involved in the development and validation of an evaluation instrument, the Science Delivery Evaluation Instrument for Māori Settings (SDEIMS),used to identify and help kura (Māori schools) in addressing factors influencing science program delivery. The study begins by exploring the themes generated from a qualitative study pertaining to the phenomenon of science delivery in eight kura that encourage science teaching from or with reference to a perspective of Te Ao Māori in the language medium of Te Reo Māori. These themes are explored through the critical lenses of Kaupapa Māori theory and Bronfenbrenner’s bio-ecological theory. Subsequent to this, quantitative procedures used to develop and validate the SDEIMS are presented. Finally, practical applications of the SDEIMS as a part of an ongoing initiative are also discussed.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".