The Elephant and the Goddess: How Water Systems Models Could Help Preserve Civil Life
Bibliographic record
Abstract
In this paper, the elephant in the room is the issue of projected longer-term population growths and declines in a finite world, while the Greek goddess Panacea is the opportunity to non-disruptively attain populations that live sustainably and solve some of the world’s most pressing problems. Particularly addressed in this paper is the potential of water systems models and modeling to facilitate a transition to planning for long-term sustainable lives of sustained quality. Anent “longer term”, for simplicity we consider both a typical period of family memory, for instance from 1880 to 2100, or 220 years, about seven or eight generations, as well as the anthropocene millenia. After setting down background definitions and introducing the underlying issues, we review key population and well-being trends, attitudes, and impacts, citing acknowledged experts. How, where, and when population and economic decline will occur is not covered; the paper rather suggests implications for water resources engineering and for water management modeling, even if the transition will be patchy in space and time. Confronting imminent degrowth, significant revisions of current water modeling practice are suggested: planned, phased, orderly removal of projected and existing urban development and drainage infrastructure and, for instance and where applicable, systematic restoration of keystone ecology and natural hydrology. Whether the imminent degrowth era will persist is uncertain, evidently. Also alluded to is degrowth’s countervailing assurance of improved well-being, providing more time for individuals to further their personal interests. The original PowerPoint presentation is at Robillynians.org and also at the CHI website (James 2023). Questions raised and the answers given at the presentation are included in the appendix to this paper.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".