Marine Prosperity Areas: a framework for aligning ecological restoration and human well-being using area-based protections
Bibliographic record
Abstract
Mechanisms for marine ecological protection and recovery, including area-based conservation tools like ‘Marine Protected Areas’ (MPAs) are necessary tools to reach the Aichi Target or the forthcoming 30x30 target set by the Kunming-Montreal Biodiversity Framework. However, full ecosystem recovery takes years to manifest and the idea that MPA protection alone will foster human well-being is frequently contradicted by socio-economic evidence. Therefore, a new framework for marine area-based conservation and ecosystem restoration that reconciles the discrepancies between ecological recovery and socio-economic growth timelines is needed to effectively meet global biodiversity conservation targets. We introduce the concept of ‘Marine Prosperity Areas,’ (MPpA) an area-based conservation tool that prioritizes human prosperity as opposed to passively relying on ecosystem recovery to catalyze social change and economic growth. This concept leverages a suite of tried-and-true community-based intervention and investment strategies to strengthen and expand access to environmental science, social goods and services, and the financial perks of the blue economy. This data-driven framework may be of interest to stakeholders who support traditional area-based conservation models, but also to those who have been historically opposed to MPAs or have been excluded from past conservation processes.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".