Sonic Spectres: Word Ghosts in Madeleine Thien’s Dogs at the Perimeter and the Digital Map Project, ‘Fictional Montreal/Montréal fictif’
Bibliographic record
Abstract
This article analyzes various ghosts and their connections with the unsaid and said in relation to Madeleine Thien’s Dogs at the Perimeter (2011) and the digital map project, ‘Fictional Montreal/Montréal fictif’ (Morgan and Lichti, 2016–17). Drawing on Jacques Derrida’s work on spectres, it suggests that Thien’s novel offers both negative and positive hauntings, by drawing attention to the far-reaching effects of the Cambodian genocide. It goes on to reflect on absence and presence, voice and body in relation to the digital map, which features recordings of authors reading extracts of their fiction set in Montréal. Arguing that ‘Fictional Montreal/Montréal fictif’ performs an interplay between material and imaginary geographies, the article proposes that the map offers the possibility of new conceptualizations of Montréal. In so doing, it argues that both it and Dogs at the Perimeter embrace the potentially utopian aspect of spectrality identified by Derrida. This is due to their encouraging readers to think about our collective responsibilities to each other in a world characterized by mobility and migration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| 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".