Reciprocal contributions in marine Indigenous stewardship: The case of Haida abalone gathering
Bibliographic record
Abstract
Abstract Throughout history, Indigenous people have stewarded seascapes, with people–nature reciprocity being a key component of many Indigenous management strategies, yet it is often overlooked in fisheries management. This research focuses on northern abalone (Haliotis kamtschatkana) in Haida Gwaii, the home of the Haida Nation. The Haida have developed a range of values associated with abalone, including reciprocity. However, commercial fishing and colonial mismanagement have led to overexploitation, resulting in a prohibition on all gatherings since 1990. Despite this, the relationship between the Haida and abalone persists in the community's knowledge. We used the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) framework and the concept of reciprocal contributions to help paint a complex relationship between Haida knowledge holders and abalone, identifying values, benefits, drivers and barriers. We identified food uses and meanings that include emotional experiences. Additionally, we compiled an array of reciprocal contributions, such as enhancement actions, fishing guidelines and nature protection practices. These memories and guidelines have been passed down through generations. The principle of ‘take only what you need to eat’ is essential for managing abalone populations. Reciprocal actions have been crucial for both abalone and the Haida. Elders have transplanted abalone to other sites, and the Council of the Haida Nation continues monitoring and restoration work. Cultural and ecological education about abalone is preserved through family stories, school activities and ceremonies, reinforcing its status as a culturally important species. The abalone fishing boom and Canadian mismanagement were identified as negative drivers that significantly impacted abalone populations. Most Haida elders recall their efforts in the 1970s to raise concerns with the federal fishing agency about the declining abalone population, but their warnings went unheeded. Currently, due to limited enforcement capacity, abalone poaching is the primary concern, while bureaucratic hurdles in decision‐making processes impede effective assessment and management. This work contributes to reviewing the past and rethinking management strategies in local contexts where values like reciprocity remain integral to Indigenous people. It also aims to generate dialogues and actions with international fisheries platforms, facilitating learning from other local experiences and recognizing common colonial barriers worldwide. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".