“A love-hate relationship”: Canadian laypeople’s construction of academic theories as diffusing innovations
Bibliographic record
Abstract
As the knowledge society paradigm and its emphasis on knowledge as socioeconomic capital grows, the importance of subtle, collective sharing of ‘blue skies’ academic theories is sidelined. Non-expert voices are largely marginalized in these discussions of what academic knowledge is or should be. In order to bridge gaps between academia and laypeople, and basic research and impact, this study explored how Canadian laypeople construed the popularity of academic theories across domains of physical sciences, social sciences, and the humanities, and related those perceptions to a novel measure of diffusion. An integrated mixed-methods research design was framed by a constructionist philosophy and a conceptual framework rooted in Diffusion of Innovations literature, re-conceptualizing academic theories as constructed innovations. Ninety academic theories were input into Google Trends, which provided data regarding the frequency with which Canadians searched theory terms. This was used as a measure of initial diffusion. Repertory grids were produced with the completion of online interviews with fifteen Canadian laypeople, which captured the qualities of academic theories that they understood as being related to popularity. Qualitative analyses revealed that Canadian laypeople had complex, mixed feelings about whether theories were beneficial or irrelevant. Cluster analyses identified themes of ‘the nature of discussion’, ‘benefits’, ‘acceptance of the best knowledge’, ‘explanation of something meaningful’, ‘understandability’, ‘physically verification’, ‘scientific proof’, ‘religious belief’, and ‘applied use’ as being used by laypeople to contrast the popularity of academic theories, the first three of which were most closely related to the initial diffusion of theories. Overall, the conceptual framework of innovations attributes from Diffusion of Innovations literature proved to be a fruitful application.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".