Triage: A new group technique gaining recognition in evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
TRIAGE, or Technique for Research of Information by Animation of a Group of Experts, is an inductive and structured method for collecting information that aims to obtain a group consensus. The goal of this technique is to provide quality informative material quickly and efficiently to enable decision-making or to develop more sophisticated survey tools. TRIAGE both distinguishes itself from, and complements, the main group techniques used in evaluation up until now. These are the Delphi technique, the Nominal Group Technique (NGT) and the focus group (Delbecq, Van de Ven & Gustafson, 1975). The definition, the context for use as well as the different parts of the usual process of TRIAGE technique (recruiting of participants, individual production phase, collective production phase with visual support, validation of results) will firstly be presented then compared to these advocated in the Delphi, NGT and focus group techniques. Also, examples of TRIAGE being applied in different evaluation contexts, such as the development of measurement instruments and the evaluation of health programs, will be presented. These examples will illustrate the richness, the flexibility and the potential of this technique as an assessment tool. Finally, the strengths and shortcomings of TRIAGE will be discussed.
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.
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 | 0.032 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it