What Gets Measured Gets Done: Challenges in Monitoring Water, Energy, and Food Security in Northern Canada
Bibliographic record
Abstract
This paper describes the challenges that were encountered during the collection of Sustainable Development Goal (SDG) indicators for water (SDG 6), energy (SDG 7), and food (SDG 2) security in northern Canada. Our findings indicate only 49% of indicator data were publicly available, while 21% had to be calculated using alternative sources or methods, 18% had to be replaced with proxy indicators for which data were available, and 12% of indicators were deemed unavailable entirely. The most common types of data challenges were associated with completeness, timeliness, and granularity. Given the current challenges faced by residents of northern Canada, with their livelihoods closely intertwined with the accessibility and availability of water, energy and food (WEF) resources, a comprehensive plan for data collection, storage, and management of WEF-related SDGs is required to advance WEF security from an aspirational to a transformative policy agenda.
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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 | 0.000 | 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 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".