Threshold concepts and sustainability: features of a contested paradigm
Bibliographic record
Abstract
Threshold concepts describe the core concepts that people must master if they are to effectively think from within a new discipline or paradigm. Here, I discuss threshold concepts relevant to the science and practice of sustainability, unpacking the persistent challenges and critiques that sustainability has faced over the decades. Sustainability is immensely popular, but also endlessly critiqued as being naïve, vague, and easy to co-opt. I argue that these challenges can be traced to sustainability’s status as a robust, alternative world view to the industrial, neoliberal paradigm. The threshold concepts discussed below are troublesome, and new learners face significant challenges when trying to learn them and move into the paradigm. Here, I review five threshold concepts that are widely discussed as important to sustainability: complexity, collaborative institutions, multiple ways of knowing, no panaceas, and adaptability. This list is not intended as comprehensive but exemplary of sustainability as a pluralistic paradigm. Recognizing the special status of these and other threshold concepts within sustainability, and the linkages and dependencies among them, is an important advance for sustainability education and practice. I also offer some suggestions on classroom activities that have proved effective in helping people through the process of learning these concepts.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".