NARCIS: The Gateway to Dutch Scientific Information
Bibliographic record
Abstract
NARCIS, National Academic Research and Collaborations Information System, is a project in the Netherlands to build a portal for research information which combines structured research information with information from OAI repositories (publication and other scientific results), websites, and news pages of research institutes. The main goal of NARCIS is to create a central place for searching all these types of data. The research data in NARCIS have been collected via the administrative processes of the different participating institutes within the work flow process. Different techniques are being used to combine the current research information and the research results. The idea for the project has been developed within the DIO-platform (National Platform Data Infrastructure Research Information) and has been realised with a subsidy of the Dutch programme DARE (Digital Academic REpositories) coordinated by SURF. SURF is the higher education and research partnership organisation for network services and information and communications technology (ICT). Partners in the NARCIS project are The Royal Netherlands Academy of Arts and Sciences (KNAW), Netherlands Organisation for Scientific Research (NWO), the Association of Universities in the Netherlands (VSNU), and the Information Centre of the Radboud University of Nijmegen (METIS). The implementing of NARCIS took one year.
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 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.131 | 0.095 |
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".