Data from: How far have we come? A review of MPA network performance indicators in reaching qualitative elements of Aichi Target 11
Bibliographic record
Abstract
This dataset is based on a systematic literature review to identify indicators used to assess MPA network effectiveness in achieving the qualitative elements. Web of Science core collection database (1900 to April 2019) and Elsevier’s Scopus database (1995 to April 2019) has been searched. Key search terms used in Web of Science and Scopus (last search date 08 April 2019 are "marine protected area network*" OR "marine reserve network*" OR "MPA network*" OR "no-take network*" OR "marine protected area system" OR "marine reserve system*” OR "MPA system*" OR "no-take system*" OR "LMMA network" OR "locally managed marine area network" OR "network of MPAs" OR "network of marine protected areas" OR “network of marine reserves” AND "effect*" OR "performance" OR “improve*” OR "success" OR “benefit" OR "enhance*" OR "impact*" OR "outcome" OR "support" OR "ecolog*" OR "abundance" OR "density” OR "size" OR "length" OR "biomass" OR “richness” OR "diversity" OR "habitat quality" OR "number" OR "social*" OR "livelihood" OR “health” OR "wellbeing" OR "well-being" OR "income" OR "employment" OR “ economic*” OR "support" OR "food security" OR “conflict” OR "participation" OR "biodiversity" OR "manage*" OR "equit*" OR “represent*“ OR "connect*" OR "integrate*" OR "governance" OR "adapt*"OR "touris*" OR "recreation". For all selected publications, we reviewed titles and abstracts to ensure that studies evaluated or discussed the effectiveness of some aspects of an MPA network or system of MPAs. Each of the final publications selected were coded for: (1) geographic location of the study; 2) one or more of the six Aichi Target 11 qualitative elements evaluated; (3) one or more of the dimensions covered by the research (ecological, social, economic, or governance); (4) the stages being evaluated in the process of effective management framework for the assessment of protected area management effectiveness; and (5) the variable(s) used to evaluate each element of the MPA network. Finally, (6) we hierarchically organized each variable into an indicator, noting that some variables were already indicators. See the related publication ( DOI: 10.1111/conl.12746 ) for more information.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.027 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".