Bibliographic record
Abstract
As everyone knows, language are invented by human beings and used by human beings. It is proved that human is the main part of language in the language creation and the language using history. Human being can be divided into two parts, male and female, and it is inevitable that language has the gender characteristic. Nowadays, information technology developed rapidly, because of its economical, efficient, user-friendly and convenient hallmarks, the Internet has irresistibly entered into almost every corner of people’s life. The result in linguistics is that a new language variety——netspeak, which was designed to meet the requirement of Computer-Mediated Communication (CMC) was created. Recent years, linguists and sociolinguists have paid increasing number of attention to netspeak. A large number of studies have been conducted on netspeak, but gender differences in netspeak have been hardly get concern because of the anonymity of CMC. In this article, I want to verify whether the previous studies findings on gender differences in face-to-face communication can be applied to describe and to explain the gender-related differences in netspeak or not, and I hope this article can be beneficial to the understanding of language and gender in CMC context.
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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".