Research in a Changing World: Normative questions and questions that matter
Bibliographic record
Abstract
Happily this is a good time to reflect on research in environmental education. It is symbolically important, 30 years after the Tbilisi Declaration (UNESCO-UNEP, 1978). It is timely in leading up to two major world conferences, the 4th World Environmental Education Congress in Durban, South Africa, and the Tbilisi+30 Conference in Ahmadabad, India. And, importantly, it feels like a geopolitically opportune time to reflect on research directions in this field, as issues like climate change and socio-environmental justice shift from the periphery towards the centre of public interest. Environmental education research has had periods of intense debate, and, to some extent, these have led to changes in direction – or, at least, widening of opportunities. To me, this has been a little reminiscent of Thomas Kuhn’s (1970) reflections; when sufficient anomalies emerge, rapid change can occur. Deeply held assumptions are revealed, challenged, and replaced. In environmental education, the phenomenon isn’t quite as tidy as this, and the story can be told in a number of ways. I don’t think we can go as far as to say that old assumptions have been replaced. But, it does seem reasonable to trace the widening of research possibilities and some twists and shifts in research priorities. The story developed in this paper draws on, what seem to me, to be three key clusters of ideas and events, presented as vignettes. They are chosen for their collective heuristic qualities. They celebrate some considerable successes in contesting once-dominant research traditions and reflecting on emergent methodologies in a context of transformation. They also point to what I think are urgent research priorities to engage in normative questions in environmental education. There are other ways to tell this research story; this is my interpretation of some of the many events of the past 30 years.
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.
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 | 0.148 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.226 |
| Scholarly communication | 0.046 | 0.093 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.032 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".