Before the Light: A discussion with Ian Rowen on the making of Transitions in Taiwan and translating narratives of the White Terror Period
Bibliographic record
Abstract
How can literature shed light onto the violence of the past? More specifically, how does Taiwanese literature in translation participate in shaping narratives of recollection of the White Terror Period in light of Taiwan’s contemporary commitment to transitional justice and global positioning as a defender of human rights? In this interview, Coraline Jortay discusses these questions with Ian Rowen, the editor of Transitions in Taiwan: Stories of the White Terror. This anthology of short stories was published in the spring of 2021 as part of the Cambria Literature in Taiwan Series, in collaboration with the National Museum of Taiwan Literature, the National Human Rights Museum and National Taiwan Normal University. The conversation touches upon the making of Transitions in Taiwan in the context of contemporary narratives of the White Terror Period and transitional justice initiatives and broader issues of positionality in translation and geopolitics.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".