Network Analysis of the Contextual Influences on Consensus-Based Decision Making and Cooperation Among and Between Local Stakeholders and a Government Agency: A Comparative Case Study of Community-based Forest Management in Ontario, Canada
Bibliographic record
Abstract
"The paper is based on a comparative case study of two Local Citizens Committees (LCCs) which advise the Ministry of Natural Resources (MNR) on the development of public forest management plans in their respective jurisdictions in the province of Ontario, Canada. It uses network, content and structural analyses to identify key context criteria, both social and physical, and analyse their content and structure of causation. Cognitive mapping and network analysis techniques are used to map context criteria and their linkages to identify key context criteria. Mapping was based on the decision maker choice perspective which considers context linkages to consensus-building to be through the beliefs of decision makers (Ford & Hegarty, 1984). Etiographic representations of the relative number of incoming links (indegree) as well as the relative number of outgoing links (outdegree) of key context criteria are then used to analyse the structure of causation among and between key context criteria and the consensus-building process for each case. This uncovers the perceived influence of MNR support staff over key context criteria and the performance and relative influence of key context criteria within a case. Key context criteria as well as their structure of causation are compared across cases and used to generate a cross-case explanation of how context influences consensus-building and the development of cooperation among and between local stakeholders and local government agencies."
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".