RENAME AND RESIST COLONIAL EXTRACTION: TWITTER’S TOPONYMICPOLITICS
Bibliographic record
Abstract
In Canada, known to Indigenous peoples as Turtle Island, the proposed Trans Mountain pipeline would more than double transport of bitumen from Alberta’s oil sands, and though national legitimation of the project is strong, it relies on economic discourse that overwrites environmental concerns, as well as Canada’s lack of consultation with Indigenous peoples. Rejecting settler-colonial authority, pipeline protestors have turned to on-the-ground tactics including petitions and demonstrations - and also to social media, where they share information, organize, and express resistance. On Twitter, user profiles are associated with a diversity of locations including places like Vancouver and Ottawa, recognized under “official,” colonial names. Twitter’s free-form location field, however, enables users to self-select their locations, and connect with one another by situating themselves according to anti-colonial or Indigenous place names, such as “Secwepemcul’ecw” and “Unceded Syilx Territory.” Considering the colonial nature of resource extraction in Canada (Preston, 2017), our project addresses: in what ways does anti-pipeline sentiment correlate with anti-colonial or Indigenous-affiliated toponymic identifiers on Twitter? Applying digital methods, we found empirically that these locations map onto the hashtag discursive space consistently with the issue alignment of discourses, suggesting that users on Twitter do indeed engage in toponymic politics (Rogers, 2015). As a space of resistance and expression of marginalized perspectives, Twitter’s free-form user location enables political expression in ways that unavailable on platforms insisting on geolocation or geotagging. By design, Twitter enables users to establish geographies of trust beyond colonial hegemony as users identify according to Indigenous place names.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".