Youth recruitment and retainment in small‐scale fisheries: Factors influencing succession and participation decisions in Cameroon
Bibliographic record
Abstract
Abstract Fisheries systems face enormous pressures from increased fish demand, decreased fish catches, and an ageing fishing population. As a case study, we investigate how climate change stressors, capacity‐building opportunities, and the introduction of climate‐smart innovations, tools and information may influence youths’ succession decisions in small‐scale fisheries (SSF). We collected empirical data from a survey with the children of SSF actors to identify the factors promoting or hindering succession in fish harvesting activities through a simple random sampling of 415 youths in six fishing communities in Cameroon. The probit model results revealed that youth participation and succession decisions are positively influenced by their education, nationality, that is, being a migrant, desire to be employed full‐time in fisheries‐related activities, climate‐smart innovations, tools and information, and capacity‐building opportunities. Increasing temperatures and uncertainty in fish availability due to climate change negatively influence their succession decisions. We find that parents do not encourage their children to participate in SSF due to climate change impacts, which are reducing fish catch and due to a lack of suitable climate‐resilient innovations and capacity‐building opportunities. The study provides evidence that interventions that create an enabling environment for youths’ participation in fisheries‐related activities are important to secure the future of SSF in Cameroon.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".