The who, what, and how of virtual participation in environmental research
Bibliographic record
Abstract
As a group of social scientists supporting a large, national, multi-site project dedicated to studying ecosystem services in natural resource production landscapes, we were tasked with co-hosting kick-off workshops at multiple locations. When, due to project design and the Covid-19 pandemic, we were forced to reshape our plans for these workshops and hold them online, we ended up changing our objectives. This redesign resulted in a new focus for our team-on the process of stakeholder and rightsholder engagement in environmental and sustainability research rather than the content of the workshops. Drawing on participant observation, surveys, and our professional experience, this perspective highlights lessons learned about organizing virtual stakeholder workshops to support landscape governance research and practice. We note that procedures followed for initiating stakeholder and rightsholder recruitment and engagement depend on the convenors' goals, although when multiple research teams are involved, the goals need to be negotiated. Further, more important than the robustness of engagement strategies is flexibility, feasibility, managing expectations-and keeping things simple.
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 | 0.135 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.032 | 0.031 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| 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".