Decolonizing bird knowledge: More-than-Western bird–human relations
Bibliographic record
Abstract
Abstract Traditional ecological knowledge (TEK) or local ecological knowledge (LEK) has only recently gained traction as “legitimate” science in Western academic discourse. Such approaches to inclusivity continue to face institutional, sociocultural, and equity barriers to being fully accepted in academic discourse in comparison to Western-based frameworks. Postcolonial studies have attempted to rectify this Western-domination in characterizing diverse forms of bird–human relationships. However, the integration of multiple cosmologies (worldviews) and ontologies (realities) in research or management creates challenges that we discuss. We elucidate commonalities and antithetical positions between Western-derived bird knowledge and management with that of TEK or LEK in both local and global contexts. We combine ecological/ornithological studies with key terms, theories, and methods from the social sciences to integrate the approaches and facilitate understanding. For example, we follow a “theory synthesis” approach in this conceptual paper to question epistemological and ontological assumptions of bird knowledge and how we acquire it to question, “how do we move from a decolonial approach (discussions and acknowledgement) to decolonization (action)?” This paper is a product of ongoing discourse among global researchers of an academic ethno-ornithology research lab based in the United Kingdom, who partner with global collaborators. The 3 case studies draw from ongoing research in Southeast Asia, South America, and decolonializing policy efforts in New Zealand. We analyzed these case studies using a postcolonial theoretical lens to provide insights into how Western scientists can embrace TEK and LEK and actively work to decolonize ethno-ornithology and ornithology in theory and practice. Further, we discuss perceived core tenets to equity and inclusion in community-based TEK and LEK conservation projects from the Global South. Diversity, equity, inclusivity, and justice in bird–human relations and knowledge were identified as targets for systemic change within the academic institutions of Western scientists. By recognizing, discussing, and embracing non-Western cosmologies and ontologies, non-Indigenous scientists can help influence the decolonization of ethno-ornithology, ornithology, and bird–human relations through respectful, participatory, equitable, culturally considerate, and “non-extractive” community-based initiatives in partnership with local groups.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".