Bibliographic record
Abstract
In 2002, the first privately protected area (PPA) was legally “certified” by the Mexican government. The last PPA country review used data from 2012, so a decadal update is considered to be timely. By June 2023, 546 land parcels within 27 states held valid certificates as PPAs or ICCAs, for a total of 718,526 ha. PPAs include 175,006 ha of private lands plus 9,860 ha of public property, which jointly represent a 44% increase from their 2012 coverage of 128,369 ha, while community lands or “territories and areas conserved by indigenous peoples and local communities” (ICCAs) now comprise 486,082 ha. No new uncertified PPA inventory has been developed to date, but their number and territorial coverage have increased. After more than 20 years of use of the certified “voluntary conservation use areas” (ADVCs) mechanism, this review gives us a clearer and more mature picture of the benefits and limitations of using this legal tool. For example, no 10-year—the initial minimum required by law—certificates remain. Meanwhile, the Kunming-Montreal Global Biodiversity Framework’s 30x30 target, with emphasis on effectively conserved and managed areas, has resulted in the development of an ADVC assessment tool, while advances toward the establishment of a legal “easement in gross” mechanism, through contractual means, have been developed for one Mexican state, which will serve as a proof-of-concept precedent for other states. Overall, certification of ADVCs has proved to be a useful tool for conservation of biodiversity and environmental services, which certainly needs to evolve to become more effective and efficient, in order to be a more widely used tool and increase its contribution for achieving Target 3 of the Kunming-Montreal Global Biodiversity Framework for Mexico.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".