Science transformed? A comparative analysis of ‘societal relevance’ rhetoric and practices in 14 Canadian Networks of Centres of Excellence
Bibliographic record
Abstract
Abstract One of the most hotly debated ideas in science studies is the claim that contemporary science is in the midst of a transformation. While ‘transformationalist’ arguments and concepts vary, their core principle is that the norms, values and practices that have enforced the separation of science from society are being challenged by new expectations that scientists pursue closer connections with industry, government and/or civil society, and address research questions of immediate value to non-academic partners. While many major funding agencies have embraced this idea and now pressure scientists to enhance the ‘societal relevance’ of their work, the impact of these changes on scientific practices is still unclear. This paper reports findings from a comparative meso-level analysis of 14 large Canadian research networks funded by an agency with an explicit transformationalist mandate – the Networks of Centres of Excellence (NCE) programme. Documents and web communications from these 14 NCEs, as well as from the central programme administration office, are analysed and compared to key transformationalist concepts, such as Mode 2 science, post-normal science, the triple helix model, academic capitalism and strategic science. We find that transformationalist ideas have a strong rhetorical presence across the 14 NCE projects and the central office, but that a great deal of inconsistency and confusion exists at the level of implementation and assessment of outcomes. Easily quantifiable outputs, such as the commercialization of research findings, are favoured over softer qualitative outcomes, such as public engagement and knowledge sharing. We conclude by arguing that the NCE programme is having an observable impact on the rhetoric of science, but any resulting transformations in practice are incremental rather than radical.
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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 | 0.042 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| 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".