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Record W2996374038 · doi:10.1139/facets-2019-0015

Is Fisheries and Oceans Canada policy receptive to a new Pacific salmon health perspective?

2019· article· en· W2996374038 on OpenAlexaffvenueabout
Julie Wittrock, Michele Anholt, Michael J. Lee, Craig Stephen

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of British ColumbiaAir CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsPerspective (graphical)FisheryEnvironmental resource managementEnvironmental planningPopulationBusinessPoliticsGeographyPublic economicsPolitical scienceBiologyEnvironmental healthEconomicsMedicine

Abstract

fetched live from OpenAlex

The concept of health permeates priorities and policies for managing wild Pacific salmon ( Oncorynchus spp.). Regulatory agencies rely largely on salmon abundance and (or) the absence of pathogens to declare if a population is healthy. Our goal was to determine if there was a receptive policy environment within Fisheries and Oceans Canada (DFO) to adopt a cumulative effects perspective of health. We used a previously developed health model along with a multiple streams framework and a narrative review of DFO policy to see how fish health was situated in DFO’s salmon management problems, policy, and politics. We discovered that a cumulative effects perspective was consistent with policy goals and priorities. DFO’s guiding principles and responsibilities for aquatic animal health were spread across multiple policies and regulations. There were no processes or people responsible for integrating information and activities. The use of the word health in policies and planning without a consistent definition meant that DFO could not explicitly assess if it has reached its management target of healthy salmon. An option for transitioning to cumulative effects perspective is to adopt a healthy public policy perspective and processes to integrate the diverse information linked to social and environmental determinants of health.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.423
Teacher spread0.379 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2019
Admission routes3
Has abstractyes

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