Theresa Cha’s Dictée as a Montage: A Visual Postcolonial-Feminist Transnational Reading
Bibliographic record
Abstract
This essay argues that Theresa Hak Kyung Cha’s multi-fragmented text Dictee would be more accessible to readers on a transnational level and more easily psychologically identified with if approached as a postcolonial-feminist silent movie which portrays Korean women’s colonial experience. The cinematic techniques Cha uses in Dictee invite the reader to read/view it as a complete (un)fragmented feminist silent movie composed of pictures, photos, film shots, and scientific diagrams. The various structures, arrangements, and juxtapositions of a significant portion of the written text suggest that some pages can be considered independent graphical images. This essay also claims that the fragmented structure of Dictee , with its many photographs, diagrams, and multiple languages, intensifies readers’ psychological identification with the text. Dictee creates a reading experience in which the reader feels alienated, confused, and sometimes helpless, which is similar to the colonial experiences of Korean women. Thus, this essay presents an (un)fragmented visual reading of the text that allows readers to identify psychologically with Korean women’s colonial experience. Despite the many languages, genres, and many fragments, the visual reading of Dictee enables readers on a transnational level to link most of the fragmented pieces together, understand the text and psychologically identify with it.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".