What's your story? Practitioners' tacit knowledge and water demand management policies in southern Africa and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it