Bibliographic record
Abstract
In response to distant suffering, global civil society is being consumed by a generalized witnessing fever that converts public spaces into veritable machines for the production of testimonial discourses and evidence. However, bearing witness itself has tended to be treated as an exercise in truth-telling, a juridical outcome, a psychic phenomenon or a moral prescription. By contrast, this article conceives of bearing witness as a transnational mode of ethico-political labour, an arduous working-through produced out of the struggles of groups and persons who engage in testimonial tasks in order to confront corresponding perils produced by instances of situational or structural violence; it is the work of witnessing, the normative and political substance generated through the performance of patterns of social action, which matters. Using Celan's allegory of the poem as a message in a bottle, I consider bearing witness as a web of cosmopolitan testimonial practices structured around five dialectically related tasks and perils: giving voice to mass suffering against silence (what if the message is never sent or does not reach land?); interpretation against incomprehension (what if it is written in a language that is undecipherable?); the cultivation of empathy against indifference (what if, after being read, it is discarded?); remembrance against forgetting (what if it is distorted or erased over time?); and prevention against repetition (what if it does not help to avert other forms of suffering?).
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".