Introducing a Framework for Open and Reproducible Research Training (FORRT)
Bibliographic record
Abstract
The Framework for Open and Reproducible Research Training (FORRT) addresses the underappreciated pedagogical aspect of open and reproducible science and its associated challenges, including a need for curricular reform, an account of epistemological pluralism, the development of new methods of education, and questions around how open science practices relate to social justice and a principled academic education. Teachers’ and researchers’ time constraints are substantial, posing a challenge to developing course materials and integrating new research practices in teaching. FORRT has developed strategies and proposed solutions to mitigate time constraints and help scholars implement open and principled education in their workflows. FORRT’s e-learning platform is a hub for community-driven initiatives and resources. FORRT’s community conceptualized our educational Nexus as integrating diverse components into one infrastructure serving those wishing to learn, adopt, and disseminate open and reproducible science tenets. In this talk, we will explain each element of FORRT’s open educational resources, FORRT’s ethos and modus operandi. Elements of the Nexus (https://forrt.org/nexus) are: FORRT’s Clusters (a pedagogically-driven organization of OS literature), Curated Resources (database of >1000 resources on OS), Initiatives Towards Social Justice in Academia (where we link mentees of unprivileged backgrounds to mentors of privileged ones), Open & Reproducible Science Summaries (having more than 300 summaries of OS literature), 7-ways to adopt principled teaching and mentoring practices (listing 100+ low commitment ways to interact with OS), Open & Reproducible Science Syllabus (a boiler-plate template for OS course that can be readily adapted and implemented in teachers’ courses), Self-Assessment Tool (a dynamic survey giving teachers feedback on how to integrate OS into their teaching), and finally, Educator’s Corner (offering a platform for educators of all stripes to share their stories, experiences, successes and hardships in teaching and mentoring, as well as for sharing educational practices and initiatives that are of interest to the OS 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 | 0.386 | 0.289 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".