The effects of institutions on perceptions of legitimacy in the Great Bear Rainforest, British Columbia
Bibliographic record
Abstract
Collaborative planning in natural resource management involves a number of non-state actors and different institutions to make decisions that fall under the realm of governance. However, legitimacy, a quality considered necessary in successful governance, has not been thoroughly investigated empirically. This research examines the perceived importance of three different dimensions of legitimacy — representativeness, meaningfulness, and effectiveness — by actors in the Great Bear Rainforest (GBR) decision-making process and the perceived roles of three institutions — shadow networks, bridging organizations, and boundary objects — in relation to the legitimacy of the GBR plan. Based on semi-structured interviews (n = 17), this research examines the perspectives of those involved or otherwise affected by the GBR decision-making process on perceived legitimacy in this context. The results illustrate the importance of representing the different participants’ interests and values in the final outcome, trustworthy relationships to build accountability and ensure commitments, strategically using representation to ensure a fair and meaningful decision-making process, and using small groups of capable negotiators to ensure that different values and interests are included at the different levels of decision-making. By analyzing the roles of shadow networks, bridging organizations, and boundary objects, these observations highlight the importance of not just representation but meaningful engagement, of actors in negotiating processes.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".