Decolonizing Indigenous science: Bees and Indigenous sovereignty
Bibliographic record
Abstract
In this conceptual paper, we argue that the assumptions behind laboratory and field studies are that chemical and compositional analysis may reveal structures unseen by means of human observation. However, replacing human observation to make it obsolete is not the purpose of science; if something can be seen, but is not measurable, that does not make it irrelevant. Although science is frequently primarily regarded as a quantitative field, we argue that qualitative data inclusion is necessary determine the consequences of research on Indigenous communities. We discuss key points, including historical and anthropocentric views of science, suggesting that Indigenous Science requires greater wisdom-based knowledge in association with traditional ecological knowledge. We introduce a new conceptual model called “Pollen Sovereignty”, a sister to Indigenous food sovereignty, to begin critical discussions around the ethics of field research and the impacts of research on the environment, land management, and Indigenous communities. That is, through simple scientific concepts, critical thought, and logic new conceptual frameworks and avenues of research, Indigenous knowledges cannot merely be coopted and reused, but respected and valued.
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 | 0.010 | 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 | 0.006 | 0.083 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".