Bibliographic record
Abstract
Text from : https://www.ouvrirlascience.fr/building-an-open-science-monitoring-framework-with-open-technologies-unesco-workshop-19-12-23/ The worldwide development of public policies promoting open science implies that indicators need to be produced to allow their monitoring. The objective to reach is to enable the measurement of the scientific production openness, as well as its impact on the scientific process itself, and ultimately for society as a whole. Until now, efforts to achieve this have mainly focused on measuring the openness of research publications as well as of data and software produced by research along with that of the results of clinical trials and publication costs. In its Recommendation on Open Science, UNESCO encourages all its member countries to implement indicators. The international nature of research makes it essential for these indicators to be geographically and institutionally consistent worldwide. Many initiatives around the world aim to gauge the openness of science. It therefore seems useful to bring these together to work towards a convergence of general principles for monitoring the progress of open science. For these reasons, France and UNESCO organised a workshop at UNESCO headquarters in Paris on December 19th 2023 to work towards achieving this objective. The day enabled international open science monitoring stakeholders to coordinate their efforts and foster the creation of an international community to drive the issue. Over fifty experts from research organisations, universities, national agencies and nonprofit organisations from three continents (in Australia, Denmark, Japan, Mexico, Germany, the Netherlands, the United States, Canada, Argentina, France, Belgium, the United Kingdom, Spain, Switzerland, Italy and Portugal) came to Paris to take part in the event. Among the many institutions represented were the CERN, NASA, CWTS, OurResearch, Crossref, DataCite, SPARC Europe, Redalyc, the OECD, COKI, the Max Plank Digital Library, PLOS, CLACSO and the Hcéres (Science and Technology Observatory). The principles for monitoring open science which the participants worked on aim to establish common guidelines for the various initiatives described above. More specifically they worked on the relevance of the indicators to be selected as well as on their transparency and reproducibility. Technical specifications will follow, aimed at bolstering the foundations of the nascent international open science monitoring community. This initiative’s objective is to simplify the implementation of open science monitoring initiatives for organisations and countries that require them. The presentations of the workshop are available below.
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
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 | MetaresearchOpen science Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchOpen science Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.185 | 0.163 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.037 | 0.064 |
| Open science | 0.009 | 0.033 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".