Interwoven Temporalities: Reading Madeleine Thien’s Dogs at the Perimeter
Bibliographic record
Abstract
This paper focuses on Madeleine Thien’s novel Dogs at the Perimeter (2011), a text that tells the stories of various characters whose lives have in distinct ways been shaped by the Khmer Rouge regime in Cambodia (1975-1979), a time in which an estimated 1.7 to 2 million people were killed or died as a result of state action. It engages with the politics of the knowledge that has been produced around this text to suggest that the challenges critics have faced while encountering the novel’s complex interwoven temporalities could provide an opportunity to engage with the limits of our various situated reading practices. It argues that Gayatri Chakravorty Spivak’s injunction to “go beyond the self-identity of nationalism towards the complex textuality of the international” may help guide us in learning to read representations of difficult histories across the North-South divide.
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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 | 0.014 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".