Mash-up, Smash-up: Mixing Genres and Mediums to Rewrite History in Do Not Say We Have Nothing
Bibliographic record
Abstract
In Madeleine Thien’s novel Do Not Say We Have Nothing, a historical photograph of three protestors at Tiananmen Square is directly inserted into the fictional text. The goal of my research is to start a scholarly conversation on this work by exploring the relationship between the historical image and the fictional text to establish Thien’s novel as postmodern. Drawing on postmodernist theories, this paper applies the works of prominent thinkers in the field to ask how the collision of genres and mediums (history and fiction; image and text), in Do Not Say We Have Nothing renders the novel postmodern. The first aim of this paper is to demonstrate the reciprocal relationship between text and image. The relationship is reciprocal because while the photograph certifies and undermines the story, the story also certifies and undermines the photograph. After establishing the multiple functions of the relationship between text and image, this paper explores how the collision of genres elicits multiple interpretations of the novel and the historical events it details. To understand how multiple interpretations of history destabilize historical metanarratives, this paper will finally investigate how the novel gives a voice to those omitted from history. By acknowledging Thien’s novel as postmodern, this paper analyzes the important role of fiction in representing those whose experiences are effaced by historical metanarratives. My postmodernist interpretation of Do Not Say We Have Nothing will provide new ways of reading and interpreting the novel and situating it within the canon of Canadian Literature.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".