Constructing Quality in Academic Science: How Basic Scientists Respond to Canadian Market-Oriented Science Policy – A Bourdieusian Approach
Bibliographic record
Abstract
Canadian science policy has increasingly linked the value of academic knowledge to its contribution to economic competitiveness. A market vision of scientific quality is embedded in new funding criteria which encourage academic scientists to collaborate with industry, generate intellectual property, and found companies. While the “Mode 2” thesis advanced by Gibbons and Nowotny asserts that quality criteria in science are changing to incorporate economic relevance, there is little empirical evidence to either refute or substantiate this claim. Using Bourdieu’s theory of practice, this study explores the responses of basic health scientists to market-oriented funding criteria. The goal of the study was to understand how scientists, occupying different positions of power in the scientific field, defined “good science” and pursued scientific prestige. Twenty semi-structured interviews were carried out with 11 scientists trained before and 9 trained after the rise of market-oriented science policy. Data derived from Curriculum Vitae and Background Information Forms were used to estimate the type and volume of capital each participant held. Scientific capital, as reflected in peer-reviewed publications and grants, was perceived as the dominant form of recognition of scientific quality. However, “entrepreneurial capital”, as reflected in patents, licenses, industry funding and company spin-offs, functioned as a new form of power in accessing resources. Study participants adopted different positions in a symbolic struggle over competing visions of “good science” and used different strategies to acquire scientific prestige. Some pursued a traditional strategy of accumulation of scientific capital, while others sought to accumulate and convert entrepreneurial capital into scientific capital. Findings suggest that there is no longer a single symbolic order in the scientific field, but that the field is stratified according to a scientific and market logic. Hence, support is provided for both continuity with “Mode 1” and change towards “Mode 2” evaluation of academic quality.
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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 | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".