Mechanization of Hearing in Chao Yuen Ren’s Dialect Research, 1927–1936: Senses, Objectivity, and Observation<xref xml:base="fn" rid="FN1"><sup>1</sup></xref>
Bibliographic record
Abstract
When scientific research began in early twentieth-century China, a key issue was the acquisition of reliable empirical information through objective and precise observations. This article examines a specific case where a scientist grappled with such an issue: the linguist Chao Yuen Ren’s application of mechanical means in his phonetic studies. In the 1920s–1930s, Chao conducted a series of field and lab studies on the dialects in southern and central China. In contrast to traditional scholars’ exclusive reliance on sharp ears and rhyme books, Chao employed mechanical devices to inscribe and analyze the spectrographs of dialectical tones and used phonographs to record the articulations of his subjects. It is demonstrated that Chao’s machines not only provided a new method of observation; they also altered the theoretical understanding of certain fundamental categories in Chinese phonology, such as tones. Moreover, Chao did not aim to replace human perception with automatic mechanisms in empirical investigations. Rather, the use of machines in his research called for an active and engaged scientific persona.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".