Writing Sound: Stenography, Writing Technology, and National Modernity in China, 1890s
Bibliographic record
Abstract
Writing practices are often subsumed under the authenticity of speech (Derrida 1976). This grammatological understanding of writing is especially conspicuous in transcription from speech to writing in institutional settings. However, transcription is not simply a “veridical record of speech” (Linell 2005) but imbued with power dynamics at the interface between writing subjects, technology, and institutions. Drawing on archival material and stenographic works from 1890s China, this article examines human‐technology interactions in Chinese stenography. The emergence of stenography coincided with the ideology of linguistic modernity, the coming of internationalization, and the political agenda of national strength, boiled down to the sensorial shift of authenticity from eye to ear in writing practices. Stenographers were seen as “transparent” mediators between speech and texts. However, the embodied labor of stenographers precludes the perfect and complete representation of sound‐in‐texts. The indexical tie between speech events and “faithful” transcripts is thus broken, complicated by negotiations between the institutional power of political‐technological rationality and executive subjects, stenographers. The legitimacy within texts, therefore, needs to be reexamined by looking into writing practices happening between stenographers and networks of institutional and technological ideologies.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".