Contribution of Indigenous Peoples' understandings and relational frameworks to invasive alien species management
Bibliographic record
Abstract
Abstract Introduced species that spread and become invasive are recognised as a major threat to global biological diversity, ecosystem resilience and economic stability. Eradication is often a default conservation management strategy even when it may not be feasible for a variety of reasons. Assessment of the substantive socioeconomic and ecological impacts of invasive alien species (IAS), both negative and positive, is increasingly viewed as an important step in management. We argue that one solution to IAS management is to align models of alien species management with Indigenous management frameworks that are relational and biocultural. We make the theoretical case that centring Indigenous management frameworks promises to strengthen overall management responses and outcomes because they attend directly to human and environmental justice concerns. We unpack the origins of the ‘introduced species paradigm’ to understand how binary framing of so‐called ‘aliens’ and ‘natives’ recalls harmful histories and alienates Indigenous stewardship. Such a paradigm thereby may limit application of Indigenous frameworks and management, and impede long‐term biodiversity protection solutions. We highlight how biocultural practices applied by Indigenous Peoples to IAS centre protecting relationships, fulfilling responsibilities and realising justice. Finally, we argue for a pluralistic vision that acknowledges multiple alternative Indigenous relationships and responses to introduced and IAS which can contribute to vibrant futures where all elements of society, including kin in the natural world, are able to flourish. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.009 | 0.007 |
| 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 | 0.031 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".