Bibliographic record
Abstract
<p><strong>About the RDA Pathways</strong></p>\n\n<p>The Research Data Alliance (RDA) Pathways were created by the RDA’s <a href="https://www.rd-alliance.org/about-rda/our-leadership/rda-technical-advisory-board.html">Technical Advisory Board</a> to help community members navigate all RDA activities. This synopsis enhances the <a href="https://www.rd-alliance.org/plenaries/rda-20th-plenary-meeting-gothenburg-hybrid">RDA’s 20th Plenary</a> experience by providing an overview of all pathways plus their relevant Plenary sessions, RDA groups, recommendations and outputs, and ambassadors.  <strong> </strong></p>\n\n<p><strong>Methodology</strong></p>\n\n<p>Machine learning classification and clustering algorithms were employed to map RDA groups, recommendations and outputs, to the Plenary pathways. A text mining exercise was undertaken to assign a list of specific keywords to each Plenary pathway using session application texts submitted by groups in 2019 and 2020. Group charters and case statement texts, harvested from the RDA website, were mined for these keywords to map groups to the Plenary pathways. Similarly, recommendations and output texts, harvested from the RDA website and Zenodo, were mined for these keywords to map recommendations and outputs to the Plenary pathways. Not all groups, recommendations and outputs were mapped to a Plenary pathway. 🔗 The data and code generated and analysed are available on <a href="https://github.com/faresdhane/RDA_Pathways">GitHub</a>.</p>
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.
How this classification was reachedexpand
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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.019 | 0.022 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.066 |
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; both teacher heads 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".