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Record W3121707469

Ocean Policy: A Canadian Case Study

2010· article· en· W3121707469 on OpenAlexaboutno aff
David VanderZwaag

Bibliographic record

VenueKnowledge@SchulichLaw · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBlueprintStatutory lawContext (archaeology)Action planGovernment (linguistics)Political sciencePublic administrationEnvironmental planningEnvironmental resource managementBusinessGeographyLawManagementEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

Over the years, Canada, like most other coastal nations, has developed an intricate set of policies and regulatory instruments focused on the management of traditional sectoral uses of the oceans. A decade ago, the necessary steps were taken to modernise the way in which Canadian authorities manage ocean-based activities. Canada did not set out to design “one” comprehensive, all inclusive oceans policy. The primary approach taken was to identify, through Canada’s Oceans Act, one federal lead authority responsible for the coordination and harmonisation of existing policy and statutory instruments and to formulate a national vision and guiding principles for oceans management within which existing and emerging policies and laws would be interpreted and implemented. This chapter outlines Canada’s statutory and policy instruments and implementation approach to oceans management. The political and environmental context within which a new management approach was developed will be described as well as the processes which led to the development of the Oceans Act, its policy framework, Canada’s Oceans Strategy and finally, the Government of Canada’s blueprint for action, Canada’s Oceans Action Plan. The relationship between key ocean-related agreements and Canadian domestic law and practice is summarised. In closing, lessons learned during the past decade will be examined, as will the challenges which lie ahead. This paper was co-authored with Camille Mageau , Fisheries and Oceans Canada ( camille.mageau@dfo-mpo.gc.ca ) and Susan Farlinger , Fisheries and Oceans Canada ( susan.farlinger@dfo-mpo.gc.ca ).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.781
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.359
Teacher spread0.335 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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