A Teacher Education for Sustainable Development System: An Institutional Responsibility
Bibliographic record
Abstract
Soft systems methodology is commonly used in organizational research and can be very useful when attempting to understand organizational structures and dynamics. A teacher education institution is identified here as an organization. Soft systems methodology is employed to gain a picture of the current organizational structure of a Science and Technology Education Department and to further develop a hypothetical picture of what the same organizational structures would look like if they incorporated ESD. These two pictures were presented to a group of teacher educators within the particular department during a focus group interview, where they were encouraged to reflect on three foci. This paper explores the teacher educators’ responses to the hypothetical system picture which elaborates on a system for teacher education for sustainable development. The paper concludes by reflecting on teacher educators’ responses and what they imply for the future of ESD at this teacher education institution. The article reveals that the following findings permeated teacher educators responses to the notional system picture for teacher education for sustainable development: (1) managements’ perceptions of professional autonomy differs from that of teacher educators; (2) there exist seven relevant sub-systems that influence teacher educators’ priority and practice; (3) teacher educators felt that research and leadership were the most powerful tools supporting the suggested ESD curriculum innovation; (4) although ESD is deemed important, it is not a priority for teacher educators due to various reasons.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".