Quantifying Institutional Reach Through the Human Network in Natural History Collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Through the Bloodhound proof-of-concept, https://bloodhound-tracker.net an international audience of collectors and determiners of natural history specimens are engaged in the emotive act of claiming their specimens and attributing other specimens to living and deceased mentors and colleagues. Behind the scenes, these claims build links between Open Researcher and Contributor Identifiers (ORCID, https://orcid.org) or Wikidata identifiers for people and Global Biodiversity Information Facility (GBIF) specimen identifiers, predicated by the Darwin Core terms, recordedBy (collected) and identifiedBy (determined). Here we additionally describe the socio-technical challenge in unequivocally resolving people names in legacy specimen data and propose lightweight and reusable solutions. The unique identifiers for the affiliations of active researchers are obtained from ORCID whereas the unique identifiers for institutions where specimens are actively curated are resolved through Wikidata. By constructing closed loops of links between person, specimen, and institution, an interesting suite of potential metrics emerges, all due to the activities of employees and their network of professional relationships. This approach balances a desire for individuals to receive formal recognition for their efforts in natural history collections with that of an institutional-level need to alter budgets in response to easily obtained numeric trends in national and international reach. If handled in a coordinating fashion, this reporting technique may be a significant new driver for specimen digitization efforts on par with Altmetric, https://www.altmetric.com, an important new tool that tracks the impact of publications and delights administrators and authors alike.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it