One Size Does Not Fit All: Canadian Government Laboratories as Diverse and Complex Institutions
Bibliographic record
Abstract
The federal government’s research laboratories are facing numerous pressures. They must support important regulatory, policy and risk-management objectives, which are critical to ensuring public confidence in the government’s ability to protect the health and safety of Canadians and the environment. Government laboratories are also cast as catalytic agents in national and local systems of innovation and are expected to contribute to industrial development. At the same time, government laboratories are under pressure to adopt new institutional arrangements and service delivery practices, and face challenges with respect to renewing their research capacities to deal with emerging science-based issues. These and other pressures create the context in which government laboratories operate. In this changing context, the roles and institutional designs of government laboratories are evolving and merit further examination. The authors introduce an analytical framework that focusses on government laboratories as complex institutions and reflects their diversity as a changing mix of hierarchies, networks and markets. They explore the links between this institutional approach and the literatures on New Public Management (NPM) and national systems of innovation. The authors then review a variety of government science policy studies from the past 40 years to determine how they have viewed the roles and institutional design of federal laboratories. Finally, the authors offer their conclusions as to the implications of the changing institutional context for Canadian science and technology policy.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.028 | 0.026 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".