How to operationalize and to evaluate the FAIRness in the crediting and rewarding processes in data sharing: a first step towards a simplified assessment grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indexed identifier ? Identification Are each data/dataset identified by an indexed and independant identifier ? Persistent metadata / data link ? Metadata traceability Are the metadata linked to the dataset through a persistent identifier? Metadata & authority linked ? Metadata traceability Are the metadata of each dataset linked to a unique authority (responsible for the datasets at a given time)? Unique, global, persistent ID? Identification Are the data identifiers unique, global and persistent ? Are the data identifiers unique, global and persistent ? Datasets linked to authority ? Metadata traceability Are all datasets linked to an authority (legal entity) through a unique and persistent identifier over time (e.g. institution, association or established body)? In case of a legal reuse restriction (such as personal data, state and public security, national defense secret, confidentiality of external relations, information systems security, secrets in industrial and commercial matters) , is the restriction properly justified?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it