Social networks and seafood sustainability governance: Exploring the relationship between social capital and the performance of fishery improvement projects
Bibliographic record
Abstract
Abstract In the early 2000s, the sustainable seafood movement put forward the concept of fishery improvement projects (FIP), a structured multi‐stakeholder approach to address environmental challenges in a fishery and aims to use the power of the market to incentivize change. The intent of the FIP model is to allow fisheries that currently do not meet the MSC standard to maintain market access while working on credible improvements. As such, FIPs have become a widely promoted approach to sustainable fisheries and have proliferated around the globe. Based on recent research assessing the impact of FIPs and testing various FIP attributes and their link to FIP performance, it seems that the FIP model may be delivering on its promise overall. However, the impact of FIP are at best based correlation rather than causation, with only few FIP attributes having been measured consistently over a significant period of time. In this theoretical contribution, we bring attention to one attribute of FIPs: the structure of their social network and its implication for social capital and successful collective action. We start by describing FIPs as projects located at the intersection of environmental governance networks and value chain network governance. Secondly, we demonstrate FIPs as complex social networks and the link between network attributes and FIP progress through the concept of social capital. Thirdly, we present the method of social network analysis and relevant network attributes to understand and characterize how FIPs work better. Finally, we suggest opportunities for further research and integration of this approach in planning and designing FIPs. Through this work, we wish to bring attention to one type of FIP attributes that is currently not explicitly being taken into account to current FIP practitioner and researchers. A free Plain Language Summary can be found within the Supporting Information of this article.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".