Bibliographic record
Abstract
Whenever developing training competencies, tools to support clinical practice or a response to a professional issue, seeking the opinion of experts is a common approach. By working to identify a consensus position, researchers can report findings on a specific question (or set of questions) that are based on the knowledge and experience of experts in their field. However, there are challenges to this approach. For example, what should be done when consensus cannot be reached? How can experts be engaged in a way that allows them to consider objectively the views of others and—where appropriate—change their own opinions in response? One approach that attempts to provide a clear method for gathering expert opinion is the Delphi technique . The Delphi technique was first developed in the 1950s by Norman Dalkey and Olaf Helmer in an attempt to gain reliable expert consensus. Specifically, they developed an approach—named after the Ancient Greek Oracle of Delphi , who could predict the future—which promoted anonymity and avoided direct confrontation between experts, so that the methods employed “…appear to be more conducive to independent thought on the part of the experts and to aid them in the gradual formation of a considered opinion ”.1 Though the original Delphi study was linked to the defence industry, the technique has spread to other research areas, including nursing.2 As with all research methods, the Delphi technique has evolved since it was first reported on in the 1960s. However, many of the fundamental characteristics of the approach still remain from Dalkey and Helmer’s original outline. First, the overarching approach is based on a …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.198 | 0.433 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".