Bibliographic record
Abstract
Abstract IR proceeds on a Eurocentric ontological assumption that sovereignty has universal validity today. How can IR be decolonised, when in spite of countless examples of the enactment of ‘sovereignty otherwise’, the discipline remains unconcerned with the fact that the logic of sovereignty remains uni -versal. The question is as much political as it is intellectual, because as a discipline, we have allowed the inertia of our professional rhythms to marginalise pluri -versal sovereignty, or the organisation of sovereignty along different ontological starting points. I argue IR must abandon its disciplinary love affair with uni-versal sovereignty. The tendency to ‘bring in’ new perspectives by inserting them into an already ontologically constituted set of assumptions works to protect IR’s Eurocentricity, which makes disciplinary decolonisation untenable. I propose that as a starting point, IR needs to be more mature about recognising the decolonisations that are happening under our very feet if we are to stand a chance at disciplinary level decolonisation. As an illustrative example, I explore an ongoing collision of settler-colonial and Mi’kmaw sovereignty through the issue of lobster fisheries in Mi’kma’ki, or Nova Scotia as the territory is known to Canadians.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".