Speaking about Weeds: Indigenous Elders’ Metaphors for Invasive Species and Their Management
Bibliographic record
Abstract
Our language and metaphors about environmental issues reflect and affect how we perceive and manage them. Discourse on invasive species is dominated by aggressive language of aliens and invasion, which contributes to the use of war-like metaphors to promote combative control. This language has been criticised for undermining scientific objectivity, misleading discourse, and restricting how invasive species are perceived and managed. Calls have been made for alternative metaphors that open up new management possibilities and reconnect with a deeper conservation ethic. Here, we turn to Indigenous perspectives because they are increasingly recognised as offering important and novel voices in invasive species discourse. We examine how Australian Aboriginal elders and land managers (rangers) speak about ‘environmental weeds’ (the term used to describe invasive plants in Australia) and weed management. Based on qualitative research with five Aboriginal groups in the Kimberley region of Western Australia, our findings indicate that Aboriginal elders speak about weeds through passive, neutral language and prefer metaphors for weed management that focus on health, care and creation. We outline the influence that this language has for how rangers practice weed work and discuss its implications for the mainstream paradigm.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".