Space for Listening: using a library unConference as an alternative method of communication
Bibliographic record
Abstract
As part of the University of Nevada, Las Vegas (UNLV) “Top Tier” initiative, the University Libraries contributes to the development of campus infrastructure and services to support research data management (RDM) and data preservation. Positioning the Libraries within the UNLV community as both partner and site for this development, we organized a faculty-oriented Research Data Management unConference during UNLV’s Research Week. The unConference attracted researchers and high-level administration from across campus and provided a forum for engagement; it was also a means for the Libraries to learn about researcher needs related to RDM, identifying potential partners, problems, and areas of support. Bridging disciplinary silos, invited speakers from academic and administrative units gave short presentations on different aspects of data management, which were followed by in-depth discussions of participant-selected topics relevant to RDM. The unConference succeeded in creating a space for meaningful interaction, with participants expressing interest in ongoing dialogue around RDM facilitated by the Libraries. Furthermore, the interactions we facilitated and feedback we received helped inform the Libraries’ next steps as we move the RDM conversation forward. This paper outlines the process of organizing and facilitating an unconference, lessons learned regarding outreach and researcher engagement, and potential pitfalls to avoid for library staff seeking to diversify their information-gathering strategies. The substance of this article is based upon poster presentations at RDAP Summit 2018 and the ALA Annual Conference and Exhibition 2018.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.029 | 0.030 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".