Bibliographic record
Abstract
Silencing is not only a physically coercive act. It is also an act of language involving forms of selection, representation and compliance. Discourse and Silencing weaves together theories and examples of discourse from different disciplines in order to put forward a theory of silencing in language: that discursive systems filter, represent and displace types of knowledge into other forms of expression. Each chapter of the book analyses examples of silencing through discourse in various social and political fields. The examples cover courtroom trials, government censorship, domestic violence, marital conversations, penal institutions, news media, and political rhetoric. They cover societies ranging from Eastern and Central Europe, Canada and the U.S. to New Zealand and Japan. The contributors clarify the difference between chosen silences and the silencing that, as a practice, seeks to limit, alter or de-legitimise another’s discourse. The book also examines the continuous resistances and shifts in discourse and silencing within the social and political frameworks in which interlocutors negotiate their relations to each other.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".