Bibliographic record
Abstract
Amid the many published pages of excited hyperbole regarding the potential of the Internet for human communications, one salient feature of current Internet communication technologies is frequently overlooked: the reality that Internet- and computer-mediated communications, to date, are communicative environments constructed through language (mostly text). In cyberspace, written language therefore mediates the human-computer interface as well as the human-human interface. What are the implications of the domination of Internet and computer-mediated communications by text? Researchers from diverse disciplines—from distance educators to linguists to social scientists to postmodern philosophers—have begun to investigate this question. They ask: Who speaks online, and how? Is online language really text, or is it “speech”? How does culture affect the language of cyberspace? Approaching these questions from their own disciplinary perspectives, they variously position cyberlanguage as “text,” as “semiotic system,” as “socio-cultural discourse” or even as the medium of cultural hegemony (domination of one culture over another). These different perspectives necessarily shape their analytical and methodological approaches to investigating cyberlanguage, underlying decisions to examine, for example, the details of online text, the social contexts of cyberlanguage, and/or the social and cultural implications of English as Internet lingua franca. Not surprisingly, investigations of Internet communications cut across a number of pre-existing scholarly debates: on the nature and study of “discourse,” on the relationships between language, technology and culture, on the meaning and significance of literacy, and on the liter
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".