The colonial legacy of herbaria
Bibliographic record
Abstract
Abstract Herbarium collections shape our understanding of the world’s flora and are crucial for addressing global change and biodiversity conservation. The formation of such natural history collections, however, are not free from sociopolitical issues of immediate relevance. Despite increasing efforts addressing issues of representation and colonialism in natural history collections, herbaria have received comparatively less attention. While it has been noted that the majority of plant specimens are housed in the global North, the extent of this disparity has not been rigorously quantified to date. Here, by analyzing over 85 million specimen records and surveying herbaria across the globe, we assess the colonial legacy of botanical collections and how we may move towards a more inclusive future. We demonstrate that colonial exploitation has contributed to an inverse relationship between where plant biodiversity exists in nature and where it is housed in herbaria. Such disparities persist in herbaria across physical and digital realms despite overt colonialism having ended over half a century ago, suggesting ongoing digitization and decolonization efforts have yet to alleviate colonial-era discrepancies. We emphasize the need for acknowledging the inconvenient history of herbarium collections and the implementation of a more equitable, global paradigm for their collection, curation, and use.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".