It takes two to tango: knowledge mobilization and ignorance mobilization in science research and innovation
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 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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.103 | 0.430 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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