Digital liaisons: Connecting diverse voices to support an ethical and sustainable information future in digital libraries
Bibliographic record
Abstract
ABSTRACT Digital Liaisons is a platform for including student and early career voices in open dialogue with practitioners and researchers who work in or adjacent to the field of Digital Libraries. It has taken several forms over the last few years, ranging from a poster session, to an unconference‐style panel, to moderated Twitter chats. This year we are proposing to merge two of these approaches: a series of Twitter chats that provide mentor‐mentee connections for early career practitioners, and a panel which reports on the outcomes of these chats and concludes with a digital poster session for students and early‐career practitioners. The merger of Twitter chats and a digital poster session will provide two ways for participants to interact with the theme of this year's conference and will allow sharing their views and experiences.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".