Tagging Objects from Colonial Contexts. A Decision Tree for the Museum für Naturkunde Berlin Collections
Bibliographic record
Abstract
The Museum für Naturkunde Berlin (MfN) has an estimated collection of 30 million items, which include zoological, mineralogical and paleontological holdings. Most of these specimens are linked to the Museum’s library, archives and associated material such as historical inventories, travel diaries, field photographs, and scientific illustrations. Even though natural history museums are not the central focus of the current public debates surrounding the colonial heritage of Western museums, these collections are nonetheless part of the colonial archive at large and are the result of interdisciplinary and cumulative practices of colonial collecting. Our sketch proposes a process to identify, check and tag holdings from colonial contexts. This should become a integral part of digitising natural history collections at MfN. The decision tree is therefore already part of developing an algorithm for tagging objects from colonial contexts.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.016 |
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; both teacher heads agree on what is shown here.
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".