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Record W4399349557 · doi:10.2305/glft9809

Clarifying 'long-term' for protected areas and other effective area-based conservation measures (OECMs): why only 25 years of 'intent' does not qualify

2024· article· en· W4399349557 on OpenAlex
James Fitzsimons, Sue Stolton, Nigel Dudley, Brent Mitchell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePARKS · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)Computer scienceGeographyPhysics

Abstract

fetched live from OpenAlex

The concept of 'long-term' is a key part of the definitions of both protected areas and other effective area-based conservation measures (OECMs). Draft principles for OECMs in Australia developed by the Australian Government propose a minimum period for OECMs of 25 years, where a landholder is not able to commit to in-perpetuity conservation. The proposal suggests this is consistent with IUCN Guidelines for Privately Protected Areas. As authors of the Guidelines for Privately Protected Areas we contend however that Australia's proposed OECM guideline suggesting 25 years of "intention" to deliver biodiversity outcomes is 'long-term' is not supported by IUCN guidelines. Furthermore for protected areas, Australia has a long-established definition of 'long-term' - specifically a minimum timeframe of 99 years is required if permanent protection is not possible - embedded in both national policy and legal agreements. As national governments rapidly seek to define OECMs in response to the raised ambitions of the Kunming-Montreal Global Biodiversity Framework, there will be increasing interest in what counts towards Target 3. Ultimately, more land managed for conservation is good and all forms of area-based conservation should be encouraged. However, not all forms of area-based conservation qualify for inclusion in Target 3. Long-term intent and outcomes are fundamental, as outlined in the definitions of protected areas and OECMs.

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.

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 score0.058
Threshold uncertainty score0.342

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)0.0000.000

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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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