The benefits and challenges of applied, partnered data-intensive research.
Bibliographic record
Abstract
ObjectivePopulation data scientists are committed to research that has public value. Much of this research is applied; it is undertaken in partnership with the public, patients, families, as well as policy- and decision-makers. Working directly with policy-makers (who are often also data providers) has advantages, but presents challenges as well. ApproachWe offer four provocations to stimulate thinking about the relationship between research and the “systems” that research is trying to influence. These provocations include: 1) assessing the implications of “partnership” and who is expected to change or accommodate others’ views, and how this affects researchers’ ability to challenge current practice; 2) challenging the emphasis on short-term over longer-term challenges in systems; 3) moving beyond post-implementation evaluations of policies; and 4) critiquing the current project-specific orientation to assessing return on investment (ROI). ResultsThe current focus on partnership in applied research tends to suggest that it is researchers who need to be empathetic to the timelines and needs of policy makers. True relationships, however, are bi-directional, and more importantly need to be open to tough conversations and constructive feedback. Further, focusing on priorities of “systems” will emphasize short-term issues. These are important to address, but can crowd out more systemic and structural considerations. This leads to researchers often engaged in post-implementation evaluation where they have had little involvement in policy or intervention design, which may not be evidence-based. Finally, a focus on single-project ROI will tend to undervalue riskier – but also potentially more rewarding – research. ConclusionIt is important to recognize that valuable research might challenge current thinking and practice, and/or address issues that are not short-term priorities. More early testing of policies before broad implementation will advance evidence. ROI should be viewed as an emergent property rather than an attribute of each individual project.
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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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | 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 | 0.636 | 0.619 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.038 | 0.048 |
| Open science | 0.009 | 0.040 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".