Reflection Paper: Recontextualising Learning? Reflections on the Social and Relational Nature of Research in Environmental Education
Bibliographic record
Abstract
The South African Research and Development Seminar (on environmental and health education) was intended to engage participants in exploring research designs from cultural/ contextual perspectives, that is, to re-inscribe their methodological practices within the fabric of social life. Participants were encouraged to present their research ‘works in progress’ as a means of ‘growing’ ideas through parallel sets of themed deliberations. Each of us knows, as active researchers, that we need a better conceptual grasp of the complexity of the inquiry process. We recognise that we must learn to operate in uncertain and indeterminate spaces so we seek new insights from diverse philosophical/theoretical notions that privilege ethics, context and socio-cultural interpretations of meaning. We also need opportunities to reflect on local interpretations of themed research issues such as participation, curriculum, and learning within broad contexts which are framed relationally (epistemologies) and which are situated. That, I think, is why we came to South Africa and why some of us will continue to find such venues that help us to trouble our work. This paper attempts to reflect on several issues that trouble our practical work, and on questions that penetrate the field and provide the basis for undoing conversations, for more thoughtful discussions that improve on the superficiality of our scholarship.
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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 | 0.076 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.063 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".