Seeing Like a Settler: Taylor Swift’s folklore and the White Visualization of Settler Colonialism
Bibliographic record
Abstract
À partir de l’album folklore de Taylor Swift (2020) considéré comme trame pour concevoir le colonialisme de peuplement en Amérique du Nord, cet article examine la signification, pour la discipline de l’histoire de l’art au Canada, de « voir comme un colon ». En explorant les connivences qui sous-tendent les fantasmes et la féminité idéalisée des pionnières blanches à travers des tropes familiers, tels que le retour à l’enfance et à la nature, je me sers de folklore pour m’interroger sur les objets de confort de l’évasion coloniale qui se sont à mon sens cristallisés dans l’isolement de la pandémie de COVID-19. Je compare les imaginaires coloniaux de folklore à ceux du photographe canadien écossais du XIX e siècle, William Notman, pour montrer que ni les fantasmes coloniaux de Swift ni les miens ne sont nouveaux, mais qu’ils s’inscrivent plutôt dans un schéma de longue haleine qui consiste à se voir comme un colon pour éviter les désagréments des complicités coloniales. En fin de compte, je me sers de cette recherche pour rejeter les fantasmes apparemment inoffensifs des femmes blanches colonisatrices comme des sources de confort non violentes auxquelles nous pouvons facilement revenir dans les moments de (relative) difficulté.
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 | 0.001 | 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 | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".