The New Brunswick Act Respecting Research Three Years Later: A Data Trickle Turns into a Flood
Bibliographic record
Abstract
IntroductionIn New Brunswick Canada (NB) in 2017, the Provincial Government passed a bill called the Act Respecting Research. This Act took the form of an omnibus bill modifying 20 pieces of legislation to define a legal authority for the New Brunswick Institute for Research, Data and Training (NB-IRDT) to receive prepared, linkable microdata from multiple public bodies in NB. This was followed in 2019 by a second Act Respecting Research that broadened the scope of the first. Together this legislation has helped to expand NB-IRDT’s data holdings from its first dataset – hospital records received in 2015 – to over 40 different linkable datasets as of March 2020. These datasets are underpinning ambitious research partnerships between NB-IRDT and the Provincial Government.
 Objectives and ApproachThis presentation will detail the rapid progress made since the first Act was passed in 2017 and presented at IPDLN-2018. It will outline enabling factors, including secured funding, the central role of the Department of Health, engagement with senior decisionmakers, and interaction with other provincial data centres and national networks including HDRN Canada. Ongoing and new challenges arising from the rapid increase in the scale of data collection and their resolution will be discussed.
 ResultsIn addition to a wide range of provincial health administrative and clinical datasets, recent datasets include school records (report cards, standardized testing, attendance), income support data, workers compensation claims data, higher education program and graduation data, court appearances, adult training/retraining programs and immigration landing records. All files are linkable at the individual level. Multi-year research projects to support program evaluation are underway, with departments now able to access other agency data through NB-IRDT.
 Conclusion / ImplicationsNB-IRDT and the experience in NB offer important lessons for other jurisdictions aiming to expand access to linkable multi-agency data for research and evaluation.
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How this classification was reachedexpand
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".