Experiencing Complexity and Retooling Understanding for Sustainability
Bibliographic record
Abstract
For Julian Edgoose the real issue is that students are being exposed to a too simplified sense of their worlds with the result that the complexity and unpredictability of their lives and of ecology is lost. He starts with the premise that schools are failing in their presentation of the issue of sustainability, not through total apathy, as some theorists have suggested, but because the diagnosis of the problem has been wrong, and then he offers an alternative analysis. The problem, he believes, lies in the narrative simplification of life exemplified in Western societies by the novel, a thesis proposed by Charles Taylor. As a result, our students and we now perceive our lives and the world to be, like the novel, simple, linear, and predictable. But this simplified view of humans and the world make it difficult, or well nigh impossible, to understand the complexity of the reality in which we are immersed and, most importantly, to think in the complex ways necessary for sustainability to become part of our North American culture.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| 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".