Bibliographic record
Abstract
This chapter addresses how to enhance the decision-making influence of IA. Four anecdotes describe applied experiences with efforts to make IA more influential. Three negative perspectives that undermine IA effectiveness are described: (1) IA is simple, static, and readily mastered, (2) IA is more trouble than it is worth, and (3) IA ends can be more effectively realized by other instruments. The legitimacy of these perspectives and measures to ameliorate and offset these perspectives are explored. The analysis is then extended by establishing a foundation (using concepts, frameworks, and research priorities) for making IA requirements and processes more relevant and influential. Selective characteristics and reforms from the four jurisdictions (the United States, Canada, Europe, and Australia), for enhancing IA decision-making influence, are presented. Process and good practice variations among IA types are considered. The contemporary challenge of good practice approaches for making IA more influential is addressed. Good practices are grouped by criteria at both the regulatory and applied levels. Major insights and lessons are highlighted.
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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 | 0.090 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".