Managing Fish or Governing Fisheries? An Historical Recount of Marine Resources Governance in the Context of Latin America – The Ecuadorian Case
Bibliographic record
Abstract
Abstract The narratives and images about ocean and its resources governance, their use and value have deep roots in human history. Traditionally, the contemporary images of fish and fisheries have been shaped under the cultural construction of power, wealth and exclusion, and also as one of poverty and marginalization. This perception was formed on early notions of natural (marine) resources access and use that were born within the colonial machinery that ruled the world from the Middle Ages until late XVII. This research explores the historical overview of marine resources usage and governance in Latin America, from a ‘critical approach to development’ perspective, by following a narrative description based on a ‘three-acts’ format. It illustrates how and to what extent politics, power and knowledge have deeply influenced policies and practices at exploring the marine and terrestrial resources and at managing fish and seafood, historically, and how the fisheries resources’ management practices are influenced by principles of appropriation, regulation and usage, put in place already in the XV century that were imposed at the conquering and colonization of the Americas, disregarded previous governance practices. This article argues that fisheries governance cannot be improved without some appreciation for the social, historical, geopolitical, and cultural significance of the fishing resources themselves, of the perceptions of them by humans, and of the interactions Global North-Global South. The analysis also opens the dialogue about what kind of ocean and governance “science” we want, to support decisions, policies and practices regarding fisheries governance. Final thoughts highlight a reflection about whose knowledge is created and used to support decision and policy making in Ecuador.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".