Bibliographic record
Abstract
In this article, I propose an exploration of the phenomenon of testimony. I argue that the phenomenological viewpoint makes possible an integrative approach to testimony, understood as a phenomenon that engages the entire existential structure of the subject, articulating the relations between language, presence, memory, truth, and temporality. The witness is not only the one who testifies, but first of all the one who faces, in experiential presence, an event whose meaning is overwhelming and thereby institutes the subject as a witness : either as an “enduring witness” if what is at stake is primarily the ownmost self of the witness as such, or as an “eyewitness” if what is at stake is mainly the other. Testimony displays its peculiar significance in the tension between the ontological pole, where the witness is understood as “being there” in the presence of the event on the experiential level, and the hermeneutical pole, where the witness is summoned in front of the others on the level of language. A further difference should be traced between a “confessing-witness,” when the testimony concerns precisely what the witness endured, and the “third-party witness,” whose bearing witness concerns what one mainly observed without enduring it oneself.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".