Key Factors in the establishment of an academia-government center of public sector administrative data and policy research
Bibliographic record
Abstract
A collaborative between the Government of New Brunswick (GNB) and the University of New Brunswick to establish a center of public sector administrative data and policy research was envisioned in 2012. Subsequent work between the parties led to the establishment of the New Brunswick Institute for Research, Data and Training (NB-IRDT) in 2014. Academia-government partnerships are not unique in Canada, however what sets this apart is: 1) the legislative approach used to support research, 2) scope of administrative data made available, 3) value placed on anonymized linked data, 4) governance overseeing the partnership, and 5) measures taken to ensure the protection of citizens’ data. In 2017, the New Brunswick Act Respecting Research received proclamation. This Act serves to provide clarity and addresses gaps in access and use of personal / health data for research. The Act has opened the doors for NB-IRDT with data owners of public sector organizations. NB-IRDT may now receive pseudonymous personal data from any public sector program collecting personal information. The partnership is governed by several advisory committees each serving a different role in overseeing the growth of NB-IRDT; overall direction setting being led by a panel of Deputy Ministers and the Clerk (the senior ranking civil servant in GNB.) The collaboration is well positioned to support public policy research and fosters the use of evidence-based information in the development of government programs and services. The partnership has also helped to encourage new and innovative thinking within GNB about the value of linkable data to support decision-making.
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 | 0.173 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.033 | 0.042 |
| Scholarly communication | 0.053 | 0.017 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".