Global research priorities for historical ecology to inform conservation
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
Historical ecology draws on a broad range of information sources and methods to provide insight into ecological and social change, especially over the past ∼12000 yr. While its results are often relevant to conservation and restoration, insights from its diverse disciplines, environments, and geographies have frequently remained siloed or underrepresented, restricting their full potential. Here, scholars and practitioners working in marine, freshwater, and terrestrial environments on 6 continents and various archipelagoes synthesize knowledge from the fields of history, anthropology, paleontology, and ecology with the goal of describing global research priorities for historical ecology to influence conservation. We used a structured decision-making process to identify and address questions in 4 key priority areas: (1) methods and concepts, (2) knowledge co-production and community engagement, (3) policy and management, and (4) climate change impacts. This work highlights the ways that historical ecology has developed and matured in its use of novel information sources, efforts to move beyond extractive research practices and toward knowledge co-production, and application to management challenges including climate change. We demonstrate the ways that this field has brought together researchers across disciplines, connected academics to practitioners, and engaged communities to create and apply knowledge of the past to address the challenges of our shared future.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it