Building an inclusive conservation vision founded upon ecological values and social opportunities
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.
Bibliographic record
Abstract
Abstract We developed a spatially explicit model for the eastern United States to help identify where we work to conserve a network of ecologically important lands with the input of communities often excluded from conservation planning. We built multiple individual and composite maps from ecological data selected from four broad themes: ecological integrity, connectivity, biodiversity, and ecosystem services. Datasets selected from these themes identify lands important for a conservation network resilient to global change stressors that provide important functions upon which people depend. We also built multiple individual and composite maps using spatial data from existing efforts to measure social conditions constructed around three broad themes: frontline communities, historically marginalized populations, and people who experience the impacts of climate change most accurately. We view these areas as having high social opportunity through improvement of the environmental conditions experienced by these communities. We also hope to engage a greater representation of values, experience, and knowledge held by communities not typically part of conservation planning. We assert that these aims can only be achieved by amplifying excluded community voice and leadership when developing approaches to conservation. This spatially explicit social-ecological model is comparable to models we have built to facilitate development of various collaboratives and initiatives in our place-based work. This is a type of decision support tool, not a decision maker. We present a broad spatial summary of three conditions: 1) co-occurring high social opportunity and nationally significant ecological value, 2) areas of high social opportunity, and 3) areas of high ecological value. These analyses are presented as a foundation for large scale and collaborative conservation planning that seeks to conserve key ecological areas while addressing the needs of a broader spectrum of people. We envision regional conservation efforts that support collective ecological and social well-being. Our framework and data can also be rescaled to smaller extents to identify projects where social and ecological well-being might intersect in local areas. While our combined spatial data synthesizes myriad information, our collection of the individual criteria can serve as a geospatial library of resources available to regional and local conservation efforts. While we intend for this work to guide conservation strategies including land protection, land management, and land stewardship initiatives in conjunction with social initiatives, this work is a tool and not itself a method or blueprint for the challenging work ahead.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it