A Little Knowledge…: Household Water Quality Investment in the Annapolis Valley
Bibliographic record
Abstract
A household drinking water and sewage management survey in Nova Scotia's Annapolis Valley suggests that ability to interpret water quality information (proxied by education) strongly affects behavior. Higher education reduces the likelihood of households treating drinking water, but may increase the amount of water treated by households choosing to do so. Septic maintenance may impact water quality, but evidence for jointness in treatment and maintenance choice is absent. Further, increased education weakly reduces septic maintenance. Results suggest that more information about septic source threats to drinking water may induce households to choose more water treatment and less septic maintenance. Information campaigns alone are therefore unlikely to significantly reduce septic source pollution . Une enquête sur la gestion de l'eau potable et des eaux usées menée auprès des ménages de la vallée d'Annapolis en Nouvelle‐Écosse autorise à penser que la capacité d'interpréter l'information sur la qualité de l'eau (déterminée par la variable ≪éducation≫) influence fortement le comportement. Le fait de posséder une éducation supérieure diminue la probabilité que des ménages à traiter l'eau potable, mais peut augmenter la quantité d'eau traitée par ceux qui choisissent de le faire. L'entretien des fosses septiques peut avoir un impact sur la qualité de l'eau, mais il n'existe pas de preuve sur le choix combiné de traiter et d'entretenir. De plus, un niveau d'éducation élevé diminue faiblement le choix d'entretenir les fosses septiques. Les résultats laissent supposer que davantage d'information sur les menaces que représentent les eaux usées pour l'eau potable peut persuader les ménages à choisir davantage de traiter l'eau et à moins entretenir les fosses septiques. Il est donc peu probable que les campagnes d'information seules réussissent à faire diminuer considérablement la pollution provenant des fosses septiques .
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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 | 0.001 | 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 | 0.001 | 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".