Language ideologies and media discourse : texts, practices, politics
Bibliographic record
Abstract
Introduction 1. Tommaso M. Milani & Sally Johnson (University of Leeds, UK) Part I: Standards and Standarisation in National and Global Contexts 2. Metalinguistic discourse in and about the media: some recent trends in Greek and German prescriptivism Spiros Moschonas (University of Athens, Greece) & Jurgen Spitzmuller (University of Zurich, Switzerland) 3. Globalising standard Spanish: the promotion of 'panhispanism' in the Spanish press, Darren Paffey (University of Southampton, UK) 4. Language games on Korean television: between globalization, nationalism and authority, Joseph Sung-Yul Park (National University of Singapore, Singapore) Part II: Planning and Policy in Media Programming 5. Planeta Brasil: language practices and the construction of space in Brazilian TV abroad, Iris Bachmann (University of Manchester, UK) 6. Sociolinguistic practices, media politics and Greek Cypriot TV series: reproducing language ideologies, Vasiliki Georgiou (University of Southampton, UK) 7. Language ideologies and state imperatives: the strategic use of Singlish in public media discourse, Michelle M. Lazar (National University of Singapore, Singapore) Part III. Media, Ethnicity and the Racialisation of Language 8. Lost in translation? Racialisation of a debate about language in a BBC news item, Adrian Blackledge (University of Birmingham, UK) 9. Metadiscourses of race in the news: the Celebrity Big Brother row, Bethan Davies (University of Leeds, UK) 10. Ideologising ethnolectal German, Jannis Androutsopoulos (King's College London, UK) Part IV: Language Ideologies in New-Media Commentary 11. 'Black and white': language ideologies in computer game discourse, Astrid Ensslin (University of Bangor, Wales) 12. Whose voices? A hypermodal approach to language ideological debates on the BBC 'Voices' website, Sally Johnson, Tommaso M. Milani & Clive Upton (all University of Leeds, UK) 13. 'It's not a telescope, it's a telephone': encounters with the telephone on early commercial sound recordings, Richard Bauman (Indiana University, Bloomington, USA) Commentary 14. Monica Heller (University of Toronto, Canada) Index Bibliography.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".