The Efficacy of Ecological Macro-Models in Preservice Teacher Education: Transforming States of Mind
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study aimed to describe and evaluate a transformative, embodied, emergent learning approach to acquiring ecological literacy through higher education. A class of teacher candidates in a bachelor of education program filled out a survey, which had them rate their level of agreement with 15 items related to ecological macro-models. Participants also completed self-efficacy measures pre- and postcourse. Overall, participants rated ecological macro-model learning very highly, and they rated the ambiguity and emergent learning approach significantly higher than information-transmission approaches. Participants’ self-efficacy regarding understanding of ecological concepts and teaching approaches also significantly increased. The main implication of the study are that the ecological macro-model approach offered participants the opportunity for emergent learning, and that educators who are interested in this deeper form of learning should consider how they might apply this approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it