What does it mean to be for a Place?
Bibliographic record
Abstract
Indigenous knowledge is a multilayered knowledge system that can effectively manage global ecosystem and biodiversity conservation. Conservation is an applied discipline with the goal of preserving the world’s biodiversity and ecosystems. However, settler–coloniser conservation practices often fail to fully examine how settler–coloniser epistemologies are centred at the expense of Indigenous conservation praxis. Evaluating how conservation practices outside of an Indigenous lens can become more inclusive and just is a critical area for research and reflection. We draw on our own experiences as early-career researchers working towards anticolonial, just and inclusive approaches to conservation science and practice by discussing what it means to be for a Place. We believe that a non-Indigenous conservationist who is for a Place advocates for inclusive stewardship with Indigenous Peoples and other marginalised communities to conserve species and ecosystems and the connections that bind communities to their landscapes. As an example of how settler–coloniser conservation practitioners can be for a Place, we discuss writing a policy statement in 2019 on behalf of the Society for Conservation Biology opposing the construction of the Thirty Meter Telescope on the summit of Mauna Kea, Hawai‘i. We describe the thought process behind our policy statement and provide examples of other actions for conservation researchers and practitioners working to be for a Place. We aim to provide our colleagues, particularly those trained in settler–coloniser conservation practices, an opportunity to identify more just practices for the Places we aspire to conserve.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.046 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".