OSC-NL and NLRN team up to collaborate on Open and Reproducible Science in the Netherlands
Bibliographic record
Abstract
With the addition of NLRN to the Dutch academic landscape, it is relevant to indicate how and where OSC-NL and NLRN differ in their strategies and roles, and how they complement one another. To this extent, OSC-NL and NLRN have published this collective statement that explains where these initiatives overlap, and what sets them apart. In short, OSC-NL is a national community of researchers and research supporters who make their OS practices visible and accessible to their peers, and provides input to policy, infrastructure and services to both local and national stakeholders. NLRN, on the other hand, is a network that brings togethers institutes, local initiatives and other stakeholders to increase the reproducibility of science, focussing on stakeholders alignment and agenda-setting. OSC-NL and NLRN share many goals, as Open Science and Reproducibility are topics that go hand-in-hand. It is therefore natural for OSC-NL and NLRN to collaborate. In fact, many members of OSC-NL are already active in NLRN, either in the NLRN steering committee or advisory board. At the NLRN Launch Event, possibilities for future collaborations were explore, for example on joint efforts to stimulate and facilitate ReproHacks.With OSC-NL and NLRN teaming-up, you can expect many new events and initiatives to stimulate Open and Reproducible Science!
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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 | 0.063 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.020 |
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".