Multilingual Eurovision meets plurilingual YouTube
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research examines ‘virtual linguistic landscapes’ (Ivković and Lotherington 2009) visible in user-generated YouTube.com comment fields associated with video clips of the Eurovision Song Contest. We begin by discussing contemporary sociolinguistic approaches and terminology (e.g. multi-, pluri-, polylingualism) attuned to complex phenomena in social media language contact zones. Following an application of these approaches to data, we argue that the emergence of a user-generated virtual linguistic landscape is usefully understood as a process of ‘linguascaping’ (Jaworski et al. 2003; Ivković 2012); i.e. linguistic engagements that propagate opinions, beliefs and ideological positions. As applied to YouTube comment fields, linguascaping suggests an agentive process of constructing and contesting possible ethno-linguistic identifications and power relations through semiotic resources.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it