A Chinese Whore Making Good Business on Gold Mountain: Using Historical Research to Create Historical Fiction
Bibliographic record
Abstract
For the 2011 – 2012 school year, I engaged in a year-long research project on Chinese sex workers in the “Wild West” during the Antebellum period. Typically brought to the United States under false pretences by members of Chinese tongs, Chinese sex workers were kept in homes and brothels starting from a very young age. Missionary groups attempted to "free” Chinese sex workers from these brothels, educate them and raise awareness of their plight. Historical accounts of these activities come exclusively from the perspective of the missionaries themselves, rather than the perspective of Chinese sex workers, which results in a biased account of Chinese sex workers as helpless girls. However, the research that I did indicates that there may have been examples of agency amongst Chinese sex workers. In order to share my research on agency amongst Chinese sex workers during the Antebellum period, I opted to write a historical fiction (a short story which was eventually published in the Queen’s Undergraduate Review). It is the process of translating historical research into historical fiction which I will focus on presenting. I rewrote the story and plot a number of times in order to maintain a balance of historical fact and creative license, and I struggled with the ethics of portraying historical characters and events for creative purposes. I learned what’s valuable for writing a successful historical fiction: exhaustive research, the understanding that realism is more impactful than sensationalism, and constant awareness of personal agenda or bias.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".