Dock to Doorstep: An Overview of Community Supported Fishery (CSF) Programs in the United States & Canada
Bibliographic record
Abstract
In response to an increasingly globalized seafood industry, Community Supported Fishery (CSF) programs have gained popularity over the last decade. Based loosely on the Community Supported Agriculture (CSA) model, CSFs have been described as one way to alter the traditional seafood supply chain by connecting fishers more directly to consumers. While there are a number of potential benefits to this marketing strategy, CSF programs can vary with respect to their goals, institutional structure, sourcing practices, distribution methods, and supplementary seafood sales, which may result in differential benefits to consumers and harvesters. To further investigate these differences and why they may occur, I conducted phone interviews with 22 CSFs, representing 56% of the CSFs currently in operation the United States and Canada. Results indicate CSF programs are diverse and greater consideration should be taken to understand the potential benefits of each unique model. To draw attention to the diversity of arrangements the term ‘CSF’ represents, and help ensure the potential benefits of particular CSF arrangements are presented accurately, three types of CSFs are identified based on the results of this study.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".