Evidence-based decision-making is a social endeavor
Bibliographic record
Abstract
Evidence-based decision-making (EBDM)—using the best available evidence from multiple sources to make informed decisions—is critical to the successful functioning of any organization. Much of the literature on EBDM has focused on the decision-maker as an individual. Yet to be most efficient and effective, EBDM requires extensive communication with others. Our review of the body of research on EBDM shows that it is typical for several individuals and groups to contribute to evidence-based decisions. These varied participants provide the skills and knowledge needed to incorporate diverse types of evidence into the decision process. We have found that conversations and interactions through three distinct networks contribute to successful EBDM. These networks consist of decision-makers and their staff and either (a) researchers in a relevant field; (b) stakeholders in the community or organization; or (c) colleagues with similar responsibilities and challenges, which we call communities of practice. Building social connections with people in each of these networks enhances a person’s ability to make good decisions for an organization. It is therefore imperative that organizations engaging in EBDM have ongoing programs and policies geared toward creating and maintaining these three critical connections.
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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 | 0.222 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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, 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".