A Preservice Teacher’s Reflections on Education for Sustainable Development in Multiple-Deprived Science Classrooms
Bibliographic record
Abstract
In the wake of the United Nations’ Agenda 2030 on sustainability, this study problematizes how conditions in multiple-deprived science classrooms are intricately connected to the sustainable development goals (SDGs). This narrative inquiry design research consisting of one participant, describes how the conditions of multiple-deprivation in science classrooms are influenced by, and in turn influence the achievement of, some of the SDGs. The narratives were contained in the reflections documented by a Bachelor of Education (BEd) preservice physical sciences teacher of his third- and fourth-year teaching practice experiences whilst conducting observations and teaching in multiple-deprived classrooms. The study was undergirded by education for sustainable development (ESD) and the SDGs as conceptual frameworks. The data collected were analysed through narrative data analysis techniques, revealing forms of deprivation in the science classroom which were driven by the SDGs related to poverty elimination, quality education, reduction of inequalities and social injustice, promotion of sustainable communities, and establishment of partnerships for goal attainment. The study findings show how the teaching and learning in multiple-deprived classrooms may pose as a challenge to the attainment of the SDGs, pointing out to some implications for practice.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".