<i>Ta</i> as an emergent language practice of audience design in CMC
Bibliographic record
Abstract
Abstract This study examines the use of ungendered third person Chinese pronoun ta in digital first-and-third person voiced discourses (i.e. small stories). The study asks what implications the script choice ta , as opposed to gendered 他 ta ‘he’ and 她 ta ‘she’, has for audience design and the facilitation of character empathy. The study draws on 131 digital texts from celebrity verified accounts on social media platform Sina Weibo in October 2015. From a Discourse Analytical perspective focused on deixis relative to the notion of empathy in storytelling, the study investigates emergent practices which involve the orthographic manipulation of gender. The study proposes that ta is an interpersonal resource whose deictic properties as a non-standard spelling are exploited as a property of audience design to facilitate an appeal to empathy. This facilitation is advanced by the script choice which offers a wider scope of reference, and thus targets a wider audience.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".