Meta-synthesis and R analysis of stakeholder engagement in food, energy, and water systems literature
Bibliographic record
Abstract
We conducted a literature review for manuscripts including food, energy, and water systems, and stakeholder engagement. Each manuscript was analyzed and the following data was entered into an Excel workbook in numerical, narrative or yes/no format: year of publication, citation of manuscript, location where research was conducted, country of residence of authors, author(s) affiliation, funding agency, whether a solution to the issue addressed in the paper was proposed or implemented, whether the authors employed a statistical or computational model, scale of solution, type of solution, types of stakeholders involved, description of when stakeholders were involved, how stakeholders were involved, and how stakeholders were identified. The variables that were analyzed using R were coded to a .csv format. Those variables include: solutions proposed, solutions implemented, type of solution, whether a computational or statistical model was used, researcher field, stakeholder types, level of stakeholder engagement measured by three scales (Ghodsvali, IAP2, and a scale developed by the authors), geographic scale of the issue addressed in the study, location of study and residence of researchers. R was used to analyze the data. Full details of the R analysis are included in a pdf file uploaded with the Excel and csv data.
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 | 0.055 | 0.278 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".