Creating an Audit and Feedback Loop to Monitor and Enhance Data Sharing in Canada
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
Submitted to the NDRIO: Call for white papers on Canada’s future DRI ecosystem, 14 December 2020: Data sharing is an integral part of open scholarship and research integrity (i.e., trustworthiness of research). Canada is embracing data sharing practice and will roll out data management mandates in the near future. The success of these policies can be assured if discipline specific training in data sharing is provided, local infrastructure is created, and a data sharing dashboard is created (and assessed) to monitor the implementation of data sharing practices. Such actions will place Canada at the top of the innovation and global leadership list. NDRIO is perfectly positioned to play a central role to enable these activities. This white paper makes the following recommendations: • That NDRIO initiate a funding competition open to the researcher community to create a dashboard to monitor data sharing and other open scholarship practices. • That a parallel to funding competition be created to for researchers to initiate discipline specific national training resources.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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