Workshop Report: Supporting inclusive and sustainable collections-based research infrastructure for systematics (SISRIS)
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
We created and delivered a workshop and symposium series for biologists at all career stages focused on the skills and practices needed to sustain natural history specimen attribution and citation. The name of the workshop and symposium series, SISRIS, reflected our ultimate goal of effecting community-level change by sharing skills and practices that can support inclusive and sustainable (collections-based) research infrastructure for systematics. We report here the rationale for SISRIS, its learning objectives for participants and its results, including the assessment of outcomes from three iterations of the workshop held in 2023. The SISRIS workshops and symposia were held in person at the annual meeting of the Association for Southeastern Biologists in Winston-Salem, North Carolina and Botany 2023 in Boise, Idaho. A stand-alone SISRIS workshop was held online later to accommodate individuals who were unable to travel to the in-person events.
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.
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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it