Developing a REPO Assessment Workbook: Supporting Open Science Communities in the Transition to Online Learning
Bibliographic record
Abstract
During the Covid-19 health emergency, the issue of scientific collaboration has received unprecedented attention and with it a renewed public interest in open science. In this context, elements of open science—such as preprints, open access to publications, and open data resources—are critically valorized as crucial components to be enhanced in preparation for future crises. Less discussed is the issue of open science pedagogy, its critical importance, and the shifts Open Science communities have experienced in the pandemic with the move to online working, teaching and learning. How do we learn to be ‘open’ in open research? The Reimagining Educational Practices for Open (REPO) project examines this question through a practitioner led exploration of how Open Science communities have navigated the transition to online and hybrid formats. In this workshop, we will review REPO’s ongoing community engagement efforts to document change and best practices in open online training and education. We then present two related outcomes of our work: 1. A framework for integrating and comparing insights from multiple communities of practice; 2. A prototype reflection and assessment workbook for open science educators working to build participatory learning communities. The workshop will include some time collaboratively thinking about how to construct assessment tools for open science learning communities as they move to online or hybrid formats.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Open science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.049 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".