Membership and Participation in our RCD Communities: What is it and how are we doing?
Bibliographic record
Abstract
The Research Computing and Data (RCD) community has coalesced over the past ten years to encompass hundreds of organizations that support both researchers and research support staff alike. While many of these organizations may rely on external funding, definitions of membership vary considerably, and their goals may include broadening participation, increasing diversity and inclusion, and performing outreach to encourage those besides "the usual suspects" to get involved. In addition, silent or absent audience members – ones who are minimally or not at all engaged – are easily overlooked. This preliminary work addresses a need for tools to help an organization know its membership, to characterize the depth of participation and engagement, and to identify and measure any untapped potential as part of its mission to maximize the capabilities of its community. We apply this approach to characterize and understand the Campus Research Computing Consortium (CaRCC) People Network community, both the membership and participation groups, including representation and diversity over time. We then further highlight those more deeply engaged via multiple approaches across various CaRCC activities. A "first draft" in developing a common tool set, we hope these methods will be adopted and improved upon by the larger RCD community.
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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 | 0.045 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.026 | 0.030 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".