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Record W2936636486 · doi:10.1139/facets-2018-0030

Canadians’ perspectives on how much space nature needs

2019· article· en· W2936636486 on OpenAlexaffvenueabout
Pamela A. Wright, Farhad Moghimehfar, Alison Woodley

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsCanadian Parks and Wilderness SocietyVancouver Island UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsTimelineGeographySet (abstract data type)Public supportPerceptionEnvironmental resource managementAsk priceSpace (punctuation)Set-asidePolitical scienceEnvironmental planningEcologyPublic relationsPsychologyEconomyComputer scienceEconomicsBiology

Abstract

fetched live from OpenAlex

Determining how much to set aside in a system of protected areas has been widely discussed. In the past, targets that have been set internationally and domestically are best described as politically driven. In recent years, there has been a call to shift towards evidence-based targets for conservation. One element that has been largely missing from this dialogue is public perception of how much to protect. We conducted an online, regionally balanced survey of just over 2000 Canadians to ask about their values for protected areas, including how much they thought was currently and should be protected. Overall, Canadians overwhelmingly agree that protected areas are necessary and think that approximately 50% of land and sea should be protected in Canada and globally. Nation-wide support for a significant increase in the amount of land/sea protected is a new finding in Canada, although consistent with applications of the same survey in other countries. As the timeline for achieving the current 2020 protected area targets approaches, countries are beginning to discuss what targets to set for the next decade. Our findings demonstrate strong public support for significantly scaling up Canada’s conservation targets, consistent with ecological evidence.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.998

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2019
Admission routes3
Has abstractyes

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