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Record W2617662142 · doi:10.1080/14649357.2015.1131482

Exploring the winners and losers of marine environmental governance/Marine spatial planning:<i>Cui bono</i>?/“More than fishy business”: epistemology, integration and conflict in marine spatial planning/Marine spatial planning: power and scaping/Surely not all planning is evil?/Marine spatial planning: a Canadian perspective/Maritime spatial planning – “<i>ad utilitatem omnium</i>”/Marine spatial planning: “it is better to be on the train than being hit by it”/Reflections from the perspective of recreational anglers and boats for hire/Maritime spatial planning and marine renewable energy

2016· article· en· W2617662142 on OpenAlexaffabout
Wesley Flannery, Geraint Ellis, Melissa Nursey‐Bray, J.P.M. van Tatenhove, Christina Kelly, Scott Coffen-Smout, Rhona Fairgrieve, Maaike Knol-Kauffman, Svein Jentoft, David Bacon, Anne Marie O’Hagan

Bibliographic record

VenuePlanning Theory & Practice · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsMarine spatial planningCorporate governanceSpatial planningPower (physics)Environmental governancePolitical scienceEnvironmental planningEnvironmental resource managementGeographyManagementEconomics

Abstract

fetched live from OpenAlex

Marine Spatial Planning (MSP) has rapidly become the most commonly endorsed management regime for sustainable development in the marine environment. MSP is advocated as a means of managing human us...

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)0.011
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0130.070
Scholarly communication0.0220.020
Open science0.0020.012
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.270
Teacher spread0.229 · 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 designQualitative
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

Citations158
Published2016
Admission routes2
Has abstractyes

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