Shared routes of mammalian kinship: Race and migration in Long Island whaling diasporas
Bibliographic record
Abstract
In this paper, I bring together the historiography of Indigenous shore whaling on Long Island with narratives of Black diaspora and whale studies to discuss shared routes of migration in the 17th-19th centuries and shared fates under colonial capitalism. Demonstrating how the extractive conquests of colonial settlers shaped the exploitative treatment of whales and the movements of social groups who lived in dependence to them, I build on Black feminists’ theoretical work and methodologies to look for interspecies, trans-oceanic navigations of survival. In doing so, I demonstrate how intimate relations between whales and whalers were shaped by processes of colonization, coastal displacement, and by conditions of indebtedness, enslavement, and fugitivity. I argue the importance of recognizing whales as mammalian kin, caught in the same net of colonial capitalist settlement and resource extraction as their hunters. Finally, inspired by the metaphor of echolocation, a method of listening which helps whales to navigate oceans, I suggest that we might listen for the socio-ecological reverberations of historic whaling diasporas to learn from emergent strategies of survival.
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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 | 0.001 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".