Wet vs Dry: Theorizing a Multilevel Water Framework for Canadian Communities
Bibliographic record
Abstract
What could be simpler than water? It is a fundamental building block of life and a daily necessity to every living being. As a substance it is, quite literally, clear, uncomplicated, basic and seemingly limitless. However, as a resource nothing rivals its complexity. The very fact that it is essential to so many and the sheer variety of ways it impacts, almost unremarked, our daily lives accounts for this intricacy. At its most basic water is elemental, but it can be conceptualized in myriad dimensions depending on ones frame of reference. For instance, water is fundamental to life, not only to the degree that it must be consumed by living organisms but can also be seen as a habitat. These habitats form part of a complex chain of ecosystems that form the organs of the planet and the engines that comprise and regulate our environments. Water geography plays an important role in shaping social histories and constructing national imaginaries (see Biro, 2007). More concretely, as a consumable resource water is mobile- it can be appropriated, diverted, removed and is therefore an important factor to the economy. In this capacity it can be seen a commodity that can be bought or sold, exported or stored. Water is also convertible – it can be harnessed to create energy, but is also a primary or intermediary input into all industries from agriculture to
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".