Views from the other side of the bird: usersâ perspectives on bird collections
Bibliographic record
Abstract
Bird collections are an increasingly valuable resource for the scientific and artistic communities. A number of recommendations are made which should facilitate legitimate use of bird collections, and help to justify the continued support of these collections. \n1. If staffing limitations hinder the legitimate use of an institution’s collections, this situation should be used as justification for better funding. \n2. Online catalogues have a valuable but limited role in facilitating research; directors of institutions must be made aware that such catalogues do not diminish the role of the curatorial staff. \n3. All stakeholders must accept their responsibility to prevent further marginalization of museum collections. \n4. In many cases the value of having rare artefacts on public display is greater than the risk to those items, and so institutions should consider placing a greater proportion of their most valuable artefacts on display to the public. \n5. Institutions should develop and publish guidelines on acceptable uses of their collections and a list of applicable fees if such fees are appropriate. These guidelines should include a description of the procedures involved in applying for access to the collection.
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 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 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".