Indigenous Peoples are critical to the success of nature-based solutions to climate change
Bibliographic record
Abstract
Nature-based solutions (NbS) to climate change mitigation—such as ecosystem protection or conservation, improved forest management practices, as well as afforestation—can significantly reduce global net emissions (Griscom et al. 2017; Seddon 2019). This is particularly the case when NbS are paired with emissions reductions and clean energy solutions (Anderson et al. 2019; Griscom et al. 2019; IPCC 2019). NbS could provide 30%–40% of the CO2 mitigation required by 2030 to help ensure warming is capped at under 2 °C (Seddon 2019). While holding great potential for effective climate change policy, some NbS measures such as protected areas and forest plantations can negatively impact Indigenous Peoples globally through displacement, livelihood restrictions, and ensuing cultural impacts (e.g., Osborne 2015; Vanclay 2017) and thus must be designed and implemented with Indigenous participation and consent if they are to be successful. These impacts intensify the disadvantages already faced by Indigenous communities, including human rights violations, discrimination, and poverty (Chatty and Colchester 2002; Dowie 2009; United Nations 2009). Indigenous Peoples are also more vulnerable to the impacts of climate change including food insecurity; displacement as a result of catastrophic flooding, drought, and fires; and threats to critical infrastructure (Havemann 2009; Ford 2012; Williams 2012; Lynn et al. 2013).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".