Digital liaisons virtual uncommons: Connecting information and people in order to enhance lives via digital librarianship
Bibliographic record
Abstract
ABSTRACT Digital librarianship is a rich space in which new practitioners often find their feet on the job, picking up needed skill sets and adapting to new technologies on the fly. Creating a space for peer information sharing and mentorship is particularly important for this community. Virtual chat sessions and unconferences are two innovative ways to connect established practitioners with less experienced students and professionals in supportive, collaborative environments. By combining these two formats, it is possible to create a more inclusive space for mentorship and peer information sharing. We propose hosting a virtual uncommons that transcends geographical locations and other physical barriers. The digital liaisons virtual uncommons (DLVU) will feature leaders in digital libraries who will act in a mentoring capacity by responding to questions posed by new information professionals throughout a series of virtual chat sessions and an in‐person unconference session. In the DLVU, students and early career professionals will be able to connect with peers and mentors in the field of digital librarianship. The virtual uncommons will be a space for informal mentoring and serendipitous networking. This session will be composed of a series of Twitter chats under the hashtag #SIGDLchats and a reflective panel structured as an attendee driven unconference in which panelists serve as moderators.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".