Building a Community of Practice for Research Data Services: Experience of CLIR/DLF E-Research Peer Network & Mentoring Group
Bibliographic record
Abstract
From March to October 2014, eight academic libraries in the United States and Canada participated in the CLIR/DLF E-Research Peer Networking & Mentoring Group (ERPNMG), a program that aimed at encouraging and building a self-reliant, mutually supportive community engaged in continuous learning about e-research support. The program consisted of a series of webinars, practical activities and virtual discussions that helped the participating institutions to evaluate, refine and further implement their research data services (RDS). In this panel the ERPNMG participants, including the library representatives and the facilitators who worked with them, will share their experiences and discuss the successes and challenges of implementing research data services while engaging in mutual learning as well as propose the next steps for the ERPNMG after the end of program. We will place our experiences in the conceptual context of communities of practice (CoP) and encourage the audience to discuss the needs and opportunities for emerging communities of practice around data.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".