E-RIHS PP User strategy and access policies
Bibliographic record
Abstract
The present deliverable is about the E-RIHS strategy and policies to enable users’ access to its physical and virtual research facilities. The first section of the present document describes the user access policy for the four E-RIHS platforms: E- RIHS ARCHLAB, E-RIHS DIGILAB, E-RIHS FIXLAB and E-RIHS MOLAB. The second section outlines the stages of the selection procedures. The access policy includes different modes according to the physical or virtual nature of the facilities involved. The first one deals with access to physical platforms such as E-RIHS ARCHLAB, E-RIHS FIXLAB and E-RIHS MOLAB. In this case, access is “Excellence-driven”, i.e. it is based on the scientific quality of the access project proposed by the user and its feasibility. The second one, “Wide access” concerns online access to digital tools and digital heritage research resources accessible via E-RIHS DIGILAB. Such strategies will also include long-term projects and their access modalities. The selection procedure for Excellence-driven access envisages an E-RIHS ERIC peer-reviewing process conducted by external experts. A local technical committee assesses the overall feasibility of the research proposal. The policies reported here comply with the relevant charters on the matter. The present deliverable is the result of joint work of the Task leader together with the sub-task leaders for E- RIHS ARCHLAB, E-RIHS DIGILAB, E-RIHS FIXLAB and E-RIHS MOLAB, with contributions by the WP5 leader and the leaders of other tasks/WPs with a scope affecting access provision and the platform coordinators.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.158 | 0.253 |
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".