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Record W2047490358 · doi:10.1163/157180809x421815

Marine Genetic Resources: Outcomes of the United Nations Informal Consultative Process (ICP)

2009· article· en· W2047490358 on OpenAlexaff
Lorraine Ridgeway

Bibliographic record

VenueThe International Journal of Marine and Coastal Law · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsPresentation (obstetrics)International lawPatienceProcess (computing)Political scienceState (computer science)Order (exchange)Perspective (graphical)Law and economicsLaw of the seaLawEnvironmental ethicsSociologyPublic international lawPsychologyBusinessComputer scienceSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Abstract This presentation analyses the outcomes of the 8th ICP meeting devoted to Marine Genetic Resources, from the perspective of policy issues arising from the international debate at that meeting. After describing the purpose and working procedures of the ICP, findings derived from the specific panel discussions and state-to-state debate during the meeting are summarized. Common elements between findings from the panels and discussions, as well as tensions among positions of states, are identified. With regard to policy and legal issues that remained contentious and unresolved, eight considerations underlying divergent views are considered. It is suggested that these divergences may themselves be rooted in further deep-seated tensions between developing and developed states, as well as between commercial benefits and environmental interests. It is concluded that considerably more international debate is necessary in order to develop a better understanding of the contentious issues and to agree on a common way forward. Future solutions at the international level will require patience and realism, but in the meantime practical measures can be put forward that would generate benefits to all states and stakeholders.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.636

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2009
Admission routes1
Has abstractyes

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