Conference Rubric Development for STEM Librarians’ Publications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it