The Delphi method as an alternative to standard committee meetings to identify ecological issues for forest ecosystem-based management: A case study
Bibliographic record
Abstract
The face-to-face committee meeting is one of the most common expert consultation methods used in forest management. However, it is also laden with disadvantages, such as potential inequity in its consideration of participant opinion and the time involvement required. This led us to evaluate another expert consultation method, the Delphi method, namely by implementing it to identify ecological issues associated with second-growth boreal forests in eastern Canada. We compared this method to the committee meeting method with regard to the time investment required and the efficiency of the consultations. In all, 21 experts participated in three rounds of our implementation of the Delphi method. Subsequently, we administered an appreciation survey comparing the participants’ attitudes vis-à-vis the two methods. These comparisons showed that Delphi was less time-consuming compared to a committee meeting consultation of comparable scope. Participants also considered the Delphi method to be fair and impartial, as all opinions were considered, which is frequently not the case in committee meetings. That said, participants believed that committee meetings allowed for a greater understanding of others’ opinions. Overall, the application of the Delphi method was shown to be a promising way of determining forest ecosystem management issues.
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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 | 0.061 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".