Shining Light on Labels in the Dark: Guidelines for Offensive Collections Materials
Bibliographic record
Abstract
The natural history collections community has made significant strides in the past decade in the digitization of their holdings. Digitization has made the data and corresponding images of collections publicly available to researchers, students, and the public. Data and images are served online by institutions’ local databases, and regional, national, and international aggregators. One challenging aspect in digitizing natural history collections is the presence of offensive language, such as racial slurs in collection and location data. We present findings from a community survey and analysis of data from relevant aggregators to assess the presence of and approach to offensive language in collections data. We also suggest initial guidelines for data warning statements and disclaimers and transcription guidelines to help preserve historical integrity of data while also supporting inclusive and safe workspaces. Please note that in writing about offensive terms found in natural history collections, we use and refer to offensive terms and include images of labels and documents to provide examples.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.001 | 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".