Methodologies for the study of change in hydrology and society
Bibliographic record
Abstract
This paper reports on the progress being made on the “Methodologies” chapter of the Panta Rhei synthesis book due in May 2023 and to be officially launched at 2023 IUGG General Assembly in Berlin. Panta Rhei cornerstone emphasis is to support policies and decision making through better understanding of phenomena that emerge from social and hydrological processes of human water relations and anticipate their future evolution. Different disciplines have different societal objectives or similar objectives with different lens within the domain of Panta Rhei. As a result different are methods used, with their respective challenges. Taking stock of extensive research conducted in the past decade in context Panta Rhei, this chapter identifies a spectrum of methods that have been used to understand and interpret human water relations, with qualitative methods at one end and quantitative methods at the other end of the spectrum. The chapter then synthesizes the methods by proposing that a phenomenon has more than one color in the qualitative-quantitative spectrum when viewed through the prism of interpretation. Taking a couple of examples of phenomena, such as Jevon's paradox, the chapter demonstrates how various methodological colors in the spectrum (qualitative methods such as focus group discussions, surveys and quantitative methods such as agent based models) are needed to interpret its emergence, e.g. as a result of a lack of disincentives in case of Jevons paradox. It is concluded that for the first time diverse disciplines are converging in their pursuit of understanding and predicting human water systems for social good and Panta Rhei has accelerated this convergence. This chapter ends with a call to action on what further methodological developments appear promising and what methods should be more widely adopted, i.e. a celebration of what has been accomplished so far.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".