Tipping points toward sustainability: The role of industrial ecology
Bibliographic record
Abstract
The discussion of tipping points (TPs) is getting increasing attention when addressing the climate and energy crisis. While industrial ecology (IE) has been playing a role in the climate and energy debates, it has so far fallen short of substantially adding to the research on TPs. This forum input highlights how the current contributions from IE have impacted the scientific community's understanding of TP. It highlights potential areas where IE engagement could be more prominent and points out avenues for achieving this. In particular, five fields of action seem pertinent: (i) engaging in theory building; (ii) closer collaboration with social sciences and economics; (iii) identifying and defining new indicators; (iv) making use of new modeling techniques; and (v) open science. Supplementary Information: The online version of this article (doi:10.1111/jiec.70000) contains supplementary material, which is available to authorized users.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".