Knowledge mobilization of social sciences and humanities research: moving beyond a "zero-sum language game
Bibliographic record
Abstract
This dissertation argues that the Social Sciences and Humanities Research Council (SSHRC) of Canada’s focus on knowledge mobilization—meant to address a “paradox of ubiquity and invisibility”—inadvertently results in another paradox: a “zero-sum language game.” While there is increasing pressure to mobilize research knowledge across stakeholder communities—government, media, community organizations and publics—work beyond academic arenas is not sufficiently recognized within academic discourse communities. Discourse theory and game theory brought together illustrate how rules of discourse govern the kinds of “moves” that are made in the language game(s) of discourse communities, however, since what is well received in one discourse community is not always well received in another, the result is a “zero-sum language game.” Two forms of discourse are presented here. The first is an introductory narrative, which frames the dissertation by providing context and disclosing some of the subtext for the doctoral work undertaken, while illustrating what is made possible when different discursive practices invite freedom and diversity of voice and style into academic discourse. The second form of discourse is the more conventional series of dissertation chapters to defend the thesis through the presentation of theory, method, data, analysis and discussion. An overview of the historical context of SSHRC policy (since 1977) and the international context of a shift toward extending the reach of research as a public good are presented. A discussion of rhetorical understandings and Burkean pentadic rhetorical analysis—identifying act, agent, agency, scene and purpose—in several SSHRC documents are presented. Discourse theory and some fundamentals of game theory are presented to explicate what is meant by the term “zero-sum language game.” Evidence of the described “zero-sum language game” is presented in a discussion of issues on tenure and promotion in the research literature and by a review of tenure and promotion policies found in the collective agreements of 38 Canadian universities. The dissertation then suggests two sets of possible changes: the first provides practical considerations that involve revisiting “service” in faculty work; the second requires a revisited understanding of the changing role of academe and academic discourse. The dissertation then concludes with a short, narrative epilogue.
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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 | 0.067 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.097 |
| Scholarly communication | 0.039 | 0.031 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".