How to speak to the masses, part I: Hồ Chí Minh's instructions to cadres and the dynamics of register formation in 20th century Vietnam
Bibliographic record
Abstract
Abstract The question of how to understand the relation between language and action lies at the heart of both philosophical pragmatics and linguistic anthropology. This same question, although framed in a very different way, also emerged as a basic concern for communist revolutionaries in Vietnam in the mid 1940s and, I contend, continues to exercise the imagination of party members and others up until the present day. Drawing inspiration from Asif Agha's definition of a (semiotic) register as a “cultural model of action,” in this essay, I consider the ways in which Hồ Chí Minh along with other high‐ranking party members sought to reform Vietnamese through a project of register formation, and thereby to transform the language into an effective instrument of mass mobilization. I suggest that this project centrally involved reconceptualizing the relationship between language and action and was pursued by, on the one hand, identifying and proscribing ways of speaking in which the connection with action was seen to be broken such that speech amounted to “mere words” and, on the other, by promoting a way of speaking in which, as the frequently used Vietnamese expression has it, “speaking goes hand‐in‐hand with doing” ( nói đi đôi với làm ).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".