Repositories of biocultural diversity: Toward best practices for empowering ethnobotany in digital herbaria
Bibliographic record
Abstract
Societal Impact Statement As herbaria digitize millions of plant specimens, ethnobotanical information associated with them is becoming increasingly accessible. These biocultural data include plant uses, names, and/or management practices of Indigenous Peoples and Local Communities (IPLCs). However, the absence of shared curatorial standards limits accessibility and use by IPLCs and others. We estimated and characterized ethnobotanical data associated with herbarium specimens and provide here key considerations for future work. We identified a proportionally small, yet collectively significant, number of ethnobotanical specimens, and call for coordinating best practices among global herbaria to locate, acknowledge, and responsibly share this information, together with source communities. Summary As herbaria digitize millions of plant specimens, those containing biocultural information are becoming increasingly accessible. This information — also known as ethnobotanical data — holds both cultural and scientific value, and may include plant uses, vernacular names, local species concepts, cultural values, and plant management practices of Indigenous Peoples and Local Communities (IPLCs). However, the lack of coordinated curatorial standards currently limits both the accessibility and effective use of this information by IPLCs, ethnobotanists, and others. To address this gap, we quantitatively estimated and characterized ethnobotanical information associated with herbarium specimens and offer key considerations to guide future work. We identified a proportionally small —yet collectively significant— number of ethnobotanical specimens, comprising approximately 1.6% of all specimen records and representing hundreds of thousands of specimens in the surveyed herbaria. We advocate for coordinating best practices to locate, acknowledge, and ethically share this information among herbaria, working together with source communities and through global cooperation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".