RESEARCH AS PUBLIC PEDAGOGY: EXPLORING THE SOCIAL IMPACTS OF DOING RESEARCH
Bibliographic record
Abstract
The social impact of research has increasingly become a priority, yet current frameworks for evaluating such impact remain limited by their reliance on quantitative metrics and output-focused models. These approaches often fail to capture the complex, dynamic processes that enable societal change. This study explores an alternative approach to research impact evaluation by framing the research process itself as a form of public pedagogy and exploring how a research process in motion confers a variety of curricula to diverse publics. Collaborating with CrewCall, a non-profit in Canada’s Screen Sector, this study uses a mixed-methods, community-engaged case study design to consider how research pedagogy interacts with, and contributes to, various forms of public learning related to CrewCall’s Social Capital study: pedagogies for the public embedded in the processes, texts, relationships, and habits of the sector that reify existing power dynamics and reinforce an exclusionary status quo; pedagogies by the public mobilized by community organizations and other ‘public intellectuals’ who publicly expose, interrogate and disrupt existing norms with the aim of enlightening the broader sector public; and pedagogies of the public, in which CrewCall’s research activities democratized sector knowledge and nurtured non-traditional spaces where hegemonic ideas could be examined, unpacked and challenged (Biesta, 2014). By framing the research process as a site of critical pedagogy, this study demonstrates how research activities may both reinforce existing power dynamics, and contribute to a growing, anti-racist and anti-oppressive critical consciousness that forges new sector ‘publics’ united in their commitment to disrupting oppressive regimes and enabling a transformative reimagining of Canada’s Screen Sector. This dissertation contributes to the fields of research evaluation, public pedagogy, qualitative methodology and community-based research by proposing a new dialectical model of research impacts that accounts for the situatedness of research within broader social, economic, and structural systems. This model challenges traditional notions of validity, arguing for a community-driven, discursive approach that aligns research quality with its transformative potential. In doing so, this study offers a reimagined framework for assessing research impacts that integrates quantitative and qualitative indicators, ultimately advancing a more inclusive, equitable approach to understanding the societal contributions of research.
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.
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 | 0.151 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.017 | 0.131 |
| Scholarly communication | 0.040 | 0.042 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.006 | 0.008 |
| 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".