ACRL-SPARC Forum: What we learned about community alignment and equity for emerging scholarly infrastructure
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
During ALA’s 2019 Midwinter Meeting hosted in Seattle, ACRL, in partnership with SPARC, hosted a panel exploring emerging models for supporting open scholarly infrastructure that places an emphasis on alignment with community values, considerations of equity, and why this is important.Heather Joseph from SPARC moderated the forum, highlighting the work and perspective of the panelists: Kristen Ratan, cofounder of Collaborative Knowledge (Coko) Foundation; Leslie Chan, associate professor, University of Toronto-Scarborough Centre for Critical Development Studies; and Ashley Farley, associate officer of knowledge and research services, Bill and Melinda Gates Foundation.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.039 | 0.164 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.000 | 0.002 |
| 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