Equity zombies in Canada’s blue economy: a critical feminist analysis for equitable policy implementation
Bibliographic record
Abstract
Introduction Ocean equity is a key aim of blue economy frameworks globally and is a pillar of the international High Level Panel for A Sustainable Ocean Economy. However, the Panel offers only a general definition of ocean equity, with limited guidance for countries. Canada, as a party to the High Level Panel’s blue economy agenda, is developing its own blue economy strategy, seeking to reshape its ocean-based industries and advocate for new ones. How equity will be incorporated across scales is not yet known but has implications for how countries like Canada will develop their ocean-based industries. This raises important questions, including what are Canada’s equity commitments in relation to its blue economy and how will they be met? Currently, the industries identified in Canada’s emerging blue economy narratives are governed through both federal and provincial legislation and policies. These will shape how equity is implemented at different scales. Methods In this paper, we examine how the term equity is defined in relevant federal and provincial legislation and look to how understandings of equity found in critical feminist, environmental justice, and climate justice scholarship could inform policy and its implementation within Canada’s blue economy. We focus on two industries that are important for Canada’s blue economy: offshore oil and marine salmon aquaculture in the Canadian province of Newfoundland and Labrador. We investigate how existing legislation and policy shapes the characterization, incorporation, and implementation of equity in these industries. Results and discussion Our analysis highlights how a cohesive approach to ocean equity across the scales of legislation and policy is needed to ensure more robust engagement with social and environmental equity issues in blue economy discourse and implementation.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".