Bibliographic record
Abstract
Purpose – This paper aims to explore how and in what context adaptive governance might work in practice in relation to climate variability through the study of two successful agri-environmental programs. Design/methodology/approach – Data were obtained through semi-structured qualitative interviews with key policy informants as well as rural agricultural producers. The adaptive attributes of two successful agri-environmental programs with a proven track record in reducing vulnerability and increasing adaptive capacity of rural producers were studied, including program responsiveness, program framing, stakeholder engagement mechanisms, and the respective roles of key actors. Findings – The adaptive governance practices of program delivery through localized government personnel and organizations increased perceived responsiveness. Mechanisms of program delivery and stakeholder participation and review changed over time as well as the framing of programs. Producers and key policy informants agreed that producers responded to concretely framed issues. A possible disconnect was discovered in the anticipated role of government in relation to meeting and responding to the climate change challenge. Practical implications – This research shows a need to study changes in programs over time in relation to the attributes of adaptive management. Differing climatic events, geographies, and government and stakeholder priorities all contribute to changes in the institutional design of programs and policies. Originality/value – This paper documents adaptive governance practices in relation to two agri-environmental programs that have successfully facilitated producer adaptation to climate variability in the past, as well as the perceptions of agricultural producers of the future role of government in relation to responding to climate change.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".