Developing research priorities with a cohort of higher education for sustainability experts
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the results of a Delphi exercise used at the Halifax Consultation in which 35 experts representing 17 countries gathered to develop research priorities for the emerging field of higher education for sustainability (HES). Design/methodology/approach The Delphi technique was used to elicit the opinions of a group of experts in order to achieve a consensus position on a research priority list through a series of questionnaires interspersed with controlled feedback. Findings The final stages of the Delphi exercise revealed 19 research theme areas that were ranked by the group to develop a final priority list. Research limitations/implications The results from each round of the Delphi give an interesting perspective on experts conceptualizations of what constitutes important research in the field. Further, the final results can be used to develop research programs and projects in the future. Practical implications Reflections on the use of the Delphi in developing research priorities can aid in the future use of this technique. Further, the results have been used as the foundation for further consultations with researchers and practitioners in this field in creating action plans for the United Nations decade of education for sustainable development. Originality/value The Halifax Consultation represents the first international meeting to focus on HES research. It is hoped that the results of the Delphi exercise conducted at the meeting will contribute to the tremendous work efforts to come and will prove to be an important component in the process of furthering the field of HES in the future.
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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 | 0.078 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".