Conference Rubric Development for STEM Librarians’ Publications
Bibliographic record
Abstract
Librarians within the Engineering Libraries Division (ELD) annually publish conference papers for the American Society for Engineering Education (ASEE). The existing ASEE rubric was not sufficient for our members, so we developed a new rubric as a charged committee for this task. We briefly discuss the sparse literature in this area, focusing on the use of rubrics and the rationale behind them. Due to this lack of literature, our committee primarily utilized additional sources such as rubrics found from other professional organizations in STEM and library fields. Our rubric is designed to encourage substantive feedback and growth of authors during the process, while clarifying the expectations for submissions. This rubric consists of overall guidance and specific needs, with flexibility for the different research methods and applications expected (i.e. work-in-progress/completed research, quantitative/qualitative, etc.). We implemented this rubric successfully for the 2021 conference cycle, but will further refine it as needed, based on feedback following future conferences. With scarce literature on conference peer review, we hope by sharing our work, others may also consider and improve their organizations’ processes.
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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 | 0.057 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".