Water Well Told: Storytelling in source water protection
Bibliographic record
Abstract
Stories are part of our every day, but do we understand what storytelling does? In Canada, source water protection (SWP) is a planning process that is predominantly applied to improve rural and Indigenous drinking water sources. During SWP locals will often contribute their expertise by reflecting upon and sharing stories with qualified professionals. To shed some light on the social interactions behind water solutions this thesis examines the functions that storytelling can have in SWP. Interviewing 16 individuals affiliated with SWP, I asked them to share with me their perceptions and recollections of storytelling in the SWP context. To interpret their insights, I developed an integrative framework for storytelling function called the Three Faucet Framework. My framework draws upon the foundational concepts of planning, water management, Indigenous water research, and medical decision-making to analyse storytelling using three layers: themes, recollections, and value. The first Faucet revealed that storytelling can perform many different functions in SWP, all of which connect people to others, people to water, or both. The second Faucet methodically coded recollections of stories and found that informal settings are important for storytelling, and the most popular function of storytelling was to share place-based knowledge. The second Faucet provided good discussion topics, but the third Faucet assigned value to storytelling. To determine value I applied cultural theory’s idea of clumsy solutions to recollections of stories and observed several different ways of framing water problems; different rationalities. The SWP process relies on people from various government departments, expertise, and cultures, thus opposing ideas often collide. The best, ‘clumsy’ solutions emerge when every voice has a chance to be heard, and my findings confirm that when given the right space, storytelling encourages this process and likely enhances Indigenous involvement in water solutions.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".