Bibliographic record
Abstract
Two unspectacular interventions, performed in central Johannesburg by Simnikiwe Buhlungu and Euridice Zaituna Kala, evidence the performativity of voice in public space. Addressing the unheard in contemporary society, they operate a shift in the way language is put to use (Cassin 2018). In paradoxical reciprocity, the action of [un]hearing comes to signify a fine-tuned form of informed and involved listening capable of bringing to the fore that which ordinarily goes by unheard or remains stifled. An "accented" way of speaking for example is inflected, shows situatedness, indicates individuated thought patterns (Coetzee 2013). This form of speech carries the legacy of historical exchange between languages and the power relations involved. It bears recognition of the multiple languages involved in the totality of any act of speech. Given current global concerns, it seems indispensable to caution that language identity cuts two ways: it is simultaneously a marker of belonging and a means of singling out those who do not belong. Side-stepping identity-politics, protesting discriminations based on language proficiency, the two interventions suggest self-transforming labour where the reader or listener may potentially perform an activist interruption of the [un]heard.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".