Rename and resist settler colonialism: Land acknowledgments and Twitter’s toponymic politics
Bibliographic record
Abstract
Connected with various resurgent and decolonizing projects, Canada has seen a surge of renaming and Indigenous land acknowledgement, which draw attention to Indigenous territories that have been overwritten through colonial naming practices. While renaming practices and land acknowledgments are contested for having merely representational effects, they may also be linked with decolonizing efforts. Our paper explores subversive (re)naming practices afforded by the free-form location identifying function on Twitter’s user profiles. It then draws a connection to issue-alignment in relation to the contested Trans Mountain pipeline as a means of considering to what extent toponymic selection is linked with actual issue alignment within the colonial context of resource extraction in Canada. We apply a mixed methods approach, based in digital methods that work with Twitter’s user profile location category. We extend our analysis through a qualitative reading of key subsets of the Twitter data, using a grounded theory approach to identify prevalent themes. In keeping with the anti-colonial nature of the tweets, we resist colonial categorization of the data and instead share an “un-typology” of Twitter toponyms, which we then connect to various expressions of anti-pipeline positioning. These mixed methods help us explore the entanglement of representational toponymic significance, infrastructural, in relation to the platform and the colonial nature of geolocational regimes online, and grounded, in relation to issue expression regarding the Trans Mountain pipeline.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".