The Political and Moral Economies of Science: A Case Study of Genomics in Canada and the United Kingdom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The term political economy describes how nations organize the production and use of wealth. Following from this definition, the political economy of science can be understood as the role that scientific activity plays, or is thought to play, in national economies and the related policies and agendas that structure this contribution. A moral economy, according to Robert E. Kohler (1), is based on the social rules and customs that regulate a community. Norms about merit, reciprocity, reward, professionalism and acceptable scientific practice all contribute to the moral economy of science. Although the political and moral economies of science operate on vastly different scales, I argue, using the case of genomics in Canada and the United Kingdom, that they are co-produced. The concept of co-production (2) is used here to emphasize that both the high-level science policy context and the local culture of genomics influence each other and that neither can be understood as the primary driver for change. Rather, evidence shows that change is occurring simultaneously at both levels and that, in the case of genomics, there is a resonance between shifting science policy and shifting scientific culture. In both Canada and the United Kingdom, science is increasingly aligned with economic growth and national innovation. Scientific activity has long been assumed to provide economic and social benefits. However, starting in the 1980s, government policies and funding regimes, particularly those coming out of the Thatcher government in the United Kingdom, began to emphasis the economic dimension of scientific research. Over the last two decades, science policy agendas in both countries have shifted from supporting research based on assumed, indirect economic and social benefit toward a model that expects direct and demonstrated economic results from research. Changes in the moral economy of science have been demonstrated through studies showing that norms, attitudes, and practices are becoming more entrepreneurial in spirit. In particular, the notion of personal financial gain is becoming compatible with traditional norms around scientific merit and reward. The adoption of entrepreneurial attitudes and activities is by no means universal; however, patenting activity and researcher surveys in the life sciences suggest that a shift in favour of commercially oriented activities is underway. (3) There are a number of bridges that link the political and moral economies of science, allowing them to exert influence on one another. Universities and academic institutions are one of the more obvious points of connection. Institutional norms and infrastructure may be oriented to either encourage or discourage entrepreneurial activities. Evidence shows that most academic institutions in Canada and the United Kingdom are consciously attempting to do the former by increasing resources allocated to technology transfer activities. (4) Another bridge between the political and moral economies of science is provided via government funding regimes. Economically oriented activities are directly supported by funding programs that require industry-university collaboration, matching funds, or the demonstration of economic or social benefit as a condition of funding. Alternately, funding regimes may indirectly encourage commercial activities through specific funding programs and selection criteria. In both Canada and the United Kingdom, the research councils, which are the government bodies responsible for funding university research, have, over the past two decades, become increasingly concerned with the economic impact of the research they fund. (5) Finally, a connection is created between the political and moral economies of science by use of science indicators (measuring, for instance, the impact of scientific publications or university patenting activity) to gage national economic performance and potential. As a result of this practice, scientific reputations and activities are being directly linked with national performance measures. …
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.049 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it