Bibliographic record
Abstract
The future E-RIHS ERIC was designed to have Quality as one of its main pillars. To ensure its high level throughout the partnership, quality criteria must be met by all organizations and research groups that may state a connection with E-RIHS. The deliverable describes the quality system proposed to be adopted by E-RIHS for the quality assessment of prospective new partners and their services and for the quality audit of existing E- RIHS partners and their services. It also outlines the process to grant external organizations, services, projects and proposals the affiliation to E-RIHS, or its support. All such procedures are based on a modular operation: the evaluation of the candidate’s internal processes, of its scientific excellence and of the quality of its services and eventual suitability for E-RIHS. The deliverable is organized in separate documents appended to the main document which includes the principles of quality assessment. The appended documents treat the assessment methodology of technical resources and digital contributions to the common database, a basic quality manual for its partners, a common KPI system, and guidelines on Ethics to be implemented throughout the partnership. The deliverable constitutes a fundamental contribution to the design of the future ERIC. It was written by the E-RIHS PP Task 2.3 Quality systems and KPIs leader in cooperation with colleagues and made available for over 90 days before submission in the Project site on D4Science for comments by all E-RIHS PP partners.
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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.176 |
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".