It takes two to tango: knowledge mobilization and ignorance mobilization in science research and innovation
Bibliographic record
Abstract
The main goal of this paper is to propose a dynamic mapping for knowledge and ignorance mobilization in science research and innovation. An underlying argument is that ‘knowledge mobilization’ science policy agendas in countries such as Canada and the United Kingdom fail to capture a critical element of science and innovation: ignorance mobilization. The latter draws attention to dynamics upstream of knowledge in science research and innovation. Although perhaps less visible, there is ample evidence that researchers value, actively produce, and thereby mobilize ignorance. For example, scientists and policymakers routinely mobilize knowledge gaps (cf. ignorance) in the process of establishing and securing research funding to argue the relevance of a scientific paper or a presentation, and to launch new research projects. Ignorance here is non-pejorative and by and large points to the borders and the limits of scientific knowing – what is known to be unknown. In addition, processes leading to the intentional or unintentional consideration or bracketing out of what is known to be unknown are intertwined with, yet remain distinct from, knowledge mobilization dynamics. The concepts of knowledge mobilization and of ignorance mobilization, respectively, are understood to be the use of knowledge or ignorance towards the achievement of goals. The value of this paper lies in its conceptualization of the mobilization of knowledge as related to the mobilization of ignorance within a complex, dynamic and symbiotic relationship in science research and innovation: it takes two to tango.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.018 | 0.043 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| 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".