Sonic technologies of Postmodernity and the grain of the voice in Robert Lepage’s <a href="http://lacaserne.net/index2.php/theatre/lipsynch/">Lipsynch</a><a href="#_edn1" name="_ednref1" title=""><sup>1</sup></a>
Bibliographic record
Abstract
This paper unpacks the relationships between the human voice and sound technologies by re-reading and re-evaluating Roland Barthes seminal essay, ‘The Grain of the Voice’ an oft cited but frequently misunderstood text, in the light of Robert Lepage’s Lipsynch (Canada, 2012). This production explicitly uses analogue and digital sound technologies to reveal the complexities and contradictions operating within the sonic economy of the performance with particular reference to the way it uses digital dubbing, miming, voice-overs and lip-reading to unsettle assumptions about the connections between the language, speech and the human voice. The paper will also unsettle any simple understanding of the voice as the locus of identity, and uncover the manner in which sonic digital technologies enable us to better apprehend ‘the body in the voice as it sings, the hand as it writes, the limb as it performs’ (Barthes, 185) while remaining sceptical about the existence of a primordial, unconstructed body.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".