Government laboratories : institutional variety, change and design space
Bibliographic record
Abstract
This study examines Canadian federal government laboratories to betterunderstand their institutional variety, changes in their institutional form, and theirinstitutional design space. Three research questions are addressed: 1) h o w have theinstitutional forms of government laboratories been reconfigured during the period 1990-2005? 2) what are the laboratories' mandates and h o w do these reconfigurations affectthe labs' ability to fulfill their mandates? and 3) h o w might science policy analysis bettertake account ofthe importance, diversity and complexity of government laboratories?Three main arguments are advanced through the analysis of the evolution ofCanadian science policy and through case studies of three Environment Canadalaboratories. The first argument is that the traditional "make or buy" and related quasimarketlens on policy analysis related to government laboratories does not adequatelycapture the increasingly formal network-based approaches, both within the federalgovernment (intra-sector networks) and with other sectors (inter-sector networks), to thedelivery of government science. Accordingly, a broader "make, buy, or collaborate" mixof choices is called for.The second argument is that government laboratories as institutions exhibit aremarkable degree of diversity that is often not clearly reflected in Canadian sciencepolicy. Policy analysis suffers from a failure to:• understand the multiple (and potentially conflicting) mandates of governmentlaboratories;appreciate that formal policy-induced networks are not necessarily the same as theinformal networks that have long characterized scientific activity; and,recognize that formal networks and quasi-market approaches, while valuable andappropriate in many ways, can create problems for the labs in delivering theirdiverse mandates.The third argument is that policy and institutional analysis of governmentlaboratories requires an analytical approach that considers their core features ashierarchies, quasi-markets and networks in the context of their mandates, but that goesbeyond this basic framework to differentiate inter-sectoral and intra-sectoral networks andreveal the more complex "institutional design space" for government laboratories. Aspecific purpose of this study, therefore, is to develop a typology that can be useful ingathering more policy-relevant information about government laboratories and indesigning informed policies for the provision of government science.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".