Bibliographic record
Abstract
The Kanata Declaration reflects a broad consensus from the Research Data Management (RDM) community on priorities for the data management component of the emerging Digital Research Infrastructure (DRI) Strategy. The intended targets are first and foremost Canadian researchers, but also the New DRI Organization (NDRIO), as well as decision makers and participants in the broader research community. This is a major revision of the 2019 Kanata Declaration which emerged from the 2019 NDSF Summit. Key updates include the incorporation of feedback from the 2020 NDSF Summit Breakout sessions, collaborative notes kept during the Summit, and feedback on each of the original Declaration Statements that were solicited via a survey tool from February to April 2020.
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 | 0.039 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.054 | 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".