Exploring Standard-Based Policy Updates Using a Modified Policy Delphi: “The Categorical Delphi Technique”
Bibliographic record
Abstract
The Delphi technique is a systematic method for evaluating anonymized expert opinions to achieve convergence on complex issues. As a modified Classical Delphi, Policy Delphi explores diverse relationships between policy options rather than forcing consensus. This study explores the idea of “Categorical Delphi", a novel adaptation of Policy Delphi.The research evaluates categorical relationships between regulatory policy and its addendum policy metrics. It also explores practitioners’ attribution of quality and value of the metrics of the addendum policy within the regulatory environment. Predefined categorical relationships between two policies are established to guide the experts’ reflection on the updates made by an addendum policy on a primary policy’s metrics. Categorical Delphi employs multiple rounds of questionnaires increasingly refining their positional consensus. Attributions of quality of the metrics and the relative importance are determined by rating and ranking the quality of the addendum metrics.The Categorical Delphi Technique effectively evaluated the categorical relationships between primary and addendum policies. The addendum metrics were perceived by faculty and managers as moderate-to-high quality additions to the quality environment. The Categorical Delphi Technique offers a robust approach for evaluating categorical relationships between a regulatory policy and a policy addendum, providing actionable insights for institutions.
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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 | 0.221 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".