Environmental sciences, sustainable development and circular economy: Alternative concepts for trans-disciplinary research
Bibliographic record
Abstract
The intermeshing of disciplines from the natural sciences, social sciences, engineering and management has become essential to addressing today's environmental challenges. Yet, this can be a daunting task because experts from different disciplines may conceptualize the problems in very different ways and use vocabularies that may not be well understood by one another. This paper explores three alternative environmental concepts used in transdisciplinary research, and outlines some of the epistemological and practical problems that each one poses. It pays particular attention to the increasingly popular concept of “circular economy”, and contrasts it with the more commonly-used concepts of “environmental sciences” and “sustainable development”. In clarifying the nature, meaning and inter-relationship of these alternative concepts, the paper helps trans-disciplinary researchers to understand the opportunities and challenges associated with each one.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.084 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".