What's your story? Practitioners' tacit knowledge and water demand management policies in southern Africa and Canada
Bibliographic record
Abstract
Water efficiency research has focused on consumption rates and the tools—for example, pricing—designed to modify consumers' demand. But municipal practitioners can also be a highly influential group and have been neglected in the conventional water demand management (WDM) research. To understand better how to make WDM policy implementation more successful, practitioners “tacit knowledge” must be identified and examined. Tacit knowledge consists of deep beliefs and values about the way the world works and is important. Grounded in practical experience, tacit knowledge is informal, unspoken and often difficult to articulate. People may not even be consciously aware of their tacit knowledge; rather, their deepest beliefs and values operate as an implicit and unquestioned background understanding that shapes how they see the world and act within it. Tacit knowledge influences why practitioners are concerned about WDM, how they act on that concern and what they say about the issue when they talk to their colleagues. Identifying and understanding the potential influence of tacit knowledge would be tremendously valuable for day-to-day practices in growing municipalities and for government agencies that are responsible for infrastructure and sustainable development. By understanding practitioners' learning processes, their rationale for action and the organizational cultures in which they operate, it will be possible to make more informed policy recommendations.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".