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Record W262810947

Public Health Protection and Drinking Water Quality on First Nation Reserves: Considering the New Federal Regulatory Proposal

2009· article· en· W262810947 on OpenAlexaff
Constance MacIntosh

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLegislationStatuteGovernment (linguistics)BusinessLegislaturePublic administrationPublic healthSafe Drinking Water ActQuality (philosophy)Environmental planningPolitical scienceWater qualityLawMedicineGeography
DOInot available

Abstract

fetched live from OpenAlex

In January 2009, the federal government issued a discussion paper that details its preferred regulatory route for enabling a legislative framework. This route is to referentially incorporate provincial legislation regarding operational standards through a framework statute, and then develop the details of the regime through regulations to be developed in consultation with First Nations over the next few years. Importantly, the opening sentence of the discussion paper's executive summary expressly connects water and public health. It reads: "The provision of safe drinking water and the effective treatment of wastewater are critical in ensuring the health and safety of First Nations people and the protection of source water on First Nation lands. Below I sketch out the current conditions and how the federal proposal suggests engaging these conditions. I conclude that although regulated standards will undoubtedly bring about improvements to public health, the proposal misses some key issues. One major failing is that the proposed regime does not address off-reserve source water protection. I suggest routes to amend this issue.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.987
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0130.008
Open science0.0050.004
Research integrity0.0390.022
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.038
GPT teacher head0.237
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2009
Admission routes1
Has abstractyes

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Same venueSSRN Electronic JournalSame topicAmerican Environmental and Regional HistoryFrench-language works237,207