National Cultures in International Communication
Bibliographic record
Abstract
Technical communication involves senders, messages, and receivers. Relationships among these three elements are predicated on informational goals; for the receiver, the need for specific information; for the sender, responding to the perceived need for information that the receiver has; and, for the message, the embodiment of the needed information. The sender, therefore, accommodates the receiver in a number of ways by adapting the information in the message so that the receiver can understand it. For example, the sender uses syntactical arrangement to communicate meta-data about the information. That is, the sender can use a more complex sentence structure to enhance the information. For example, a simple sentence communicates a meaning about something; a complex sentence communicates a meaning about the meaning, with the dependent element functioning to provide background or, in some cases, cause for the independent element. Behind the adaptation is the sender’s understanding of how the language works to provide meaning. Should the receiver not be familiar with the content, the sender places undue stress on the receiver by using a more complex presentation style that leaves the receiver struggling with both the content and the means of expression. Added to these problems are the problems of the receiver’s attitude toward, among other things, the subject, the sender, and the medium, and the cultural contexts of both the sender and the receiver.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".