Through the legal maze: An Act Respecting Research
Bibliographic record
Abstract
IntroductionThe New Brunswick Institute for Research, Data and Training (NB-IRDT) is a recently established provincial research data centre and data custodian hosting anonymized linkable administrative data from the Government of New Brunswick (GNB) and other public bodies. GNB has committed to transferring research-relevant data from across GNB operations to NB-IRDT. Objectives and Approach Although NB-IRDT had received a small number of administrative data sets from the GNB Department of Health as of the end of 2016, transfers of other datasets from the Department of Health, Department of Social Development and other Departments was halted because of a series of legal opinions citing a lack of legislative authority to do so. This presentation details an innovative and transformative approach that overcame these obstacles to facilitate continued data sharing with NB-IRDT not just from those Departments but from across the spectrum of government operations. ResultsPassed in the NB Legislature in March 2017 and proclaimed in May 2017, An Act Respecting Research modified 12 different pieces of existing legislation to define a clear legal authority through which pseudo-anonymized data from all of the Provincial Government plus numerous other public bodies could be transferred to NB-IRDT in linkable form. This included Acts as disparate as the Education Act, Mental Health Act, the Nursing Homes Act, the New Brunswick Housing Act, etc. An Act Respecting Research was the culmination of more than a year of collaborative effort between NB-IRDT and the Executive Council Office plus 14 different provincial line departments. The Act also permits the collection of the Medicare health insurance numbers by departments and public for data matching and transfer purposes. Conclusion/ImplicationsThe Act Respecting Research is unique in Canada and would not have occurred without GNB’s commitment to the principle and practice of evidence-based policymaking. After the Act’s passage, NB-IRDT has received numerous datasets and work is ongoing on many more, from postsecondary education to road accidents and workers compensation claims.
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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 | 0.150 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.093 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.051 | 0.059 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".