Herbarium data and herbaria as extended archives for humanities research
Bibliographic record
Abstract
Herbaria house essential resources of value to disciplines across the university. Engagements with herbaria in the humanities often escape the attention of botanists and university leadership, despite their potential to attract more audiences and demonstrate the broader value of herbaria for their home institutions. This article describes recent developments in humanities research with herbaria and offers perspectives on ways to broaden the accessibility and utility of herbaria for humanities scholars, especially in the digital humanities. It thus explores how herbarium sheets, their labels, marginal notes, drawings, and photos constitute valuable forms of documentation for humanities research, especially in addressing colonial legacies and historical omissions. We conceptualize herbaria as “extended archives” that encompass multiple layers of documentation. The article focuses on digital remediation strategies, summarizing recent developments, and offering methodological considerations for making botanical archives more accessible to humanist researchers.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".