Digging Deeper with Delphi: The Four Step Alberta Approach
Bibliographic record
Abstract
Originally the Delphi method was created as a systematic research process for establishing agreement and structured forecasts in groups of experts. The method is based around the idea that the agreed judgement from several experts is more accurate and valuable than the judgement from a single expert. In a traditional Delphi study, the selected experts respond to several rounds of questionnaires with aggregated and shared answers among the expert group. A highlighted strength of the Delphi method is its ability to progress into new forms and implementations. Delphi studies have been used for different purposes such as identifying trends, creating guidelines and to develop theory. The aim of this study is to describe and discuss the Delphi study approach that has been developed by researchers in Alberta, Canada. In an effort to dig deeper into the ongoing transformation of higher education for technology enhanced and lifelong learning, the four steps were further modified in a Swedish Canadian study. In a qualitative Delphi study, the four steps were implemented as 1) A literature study to explore the chosen topic, with the selected publications sent out to the expert panel, 2) A survey with questions to the experts based on the findings in the literature study, 3) Email interviews to dig deeper into the answers from the survey, and finally 4) Focus group interviews based on the results from the previous steps. Findings from the various steps have been presented at conferences and published in research journals. The conclusion is that this modified and extended Delphi process has generated a rich set of data that can be used to develop a theoretical framework. At the same time the presented four step approach is time consuming and requires a research team that can work together during a longer time period.
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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 | 0.135 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".