Research Platforms: Two Diverse Sites-Similarities and Differences
Bibliographic record
Abstract
Research platforms-efforts providing data and tools to support a number of interrelated, typically longitudinal projects-generate economies of scale for policy research and intellectual synergies. The data science underlying these platforms represents a fascinating combination of political, organizational, “big data”, and design factors. Two highly regarded place-based research platforms-one focused on understanding health and human development (the Manitoba Centre for Health Policy, MCHP) and the other facilitating ecology and environmental studies (the Experimental Lakes Area, IISD-ELA) deal with dissimilar scientific and policy problems yet share a number of common elements. Comparisons demonstrate the wide-ranging opportunities for policy research which such place-based platforms create. These platforms enable both experiments and quasi-experiments, make more covariates available, and allow longer follow-up and larger case counts. They generate collaboration and facilitate improving data quality. Regardless of disciplinary roots, platforms expand research opportunities.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".