Bibliographic record
Abstract
How research evidence comes to have impacts in academic and nonacademic arenas is increasingly becoming a focal point in scholarly discourse across scholarly disciplines and jurisdictional boundaries. However, despite the growing recognition that research impact is a product of collaboration among a variety of research stakeholders, researchers remain saddled with the majority of impact responsibilities. The purpose of this policy analysis is to utilize the empirical research and contemporary politics concerning research impact to outline policy alternatives for how impact responsibilities can be reconceptualized in Canada. I begin the analysis with an overview of influential and thought-provoking research impact milestones related to legislation, research funding, and media coverage. I then outline several publication and evaluation milestones related to research impact, current system characteristics and impact constraints for social science research in Canada, and salient political viewpoints related to research impact for the relevant stakeholder groups. Four policy alternatives are presented: (1) to let present trends continue undisturbed (i.e., the status quo), (2) to provide inducements for the Social Science and Humanities Research Council of Canada to establish a knowledge mobilization and research impact department, (3) to undertake regulatory action on all Canadian universities who receive the Research Support Fund, and (4) to establish multifaceted interventions for enhancing research impact. Each alternative is evaluated across five criteria: efficiency, political viability, operational feasibility, robustness and improvability, and equitable distribution of responsibilities. Based on this outcome analysis, I make a recommendation regarding the optimal policy alternative.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Other About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Commentary About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
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 | 0.108 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.048 | 0.018 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.025 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".