Speaking from Silence: On the Intimate Relation Between Silence and Speaking
Bibliographic record
Abstract
In understanding the world through language, silence, regarded simply as the absence of speech, appears to be the enemy of understanding. But in fact, it can be shown that silence is always a function of language. As we learn from Merleau-Ponty, Heidegger, and others, the relation between silence and the word of language is a positive one. There are acts of silence that can generate the movement towards meaning in language. The focus of my remarks in this paper will explore three modalities of silence that characterize the positive relation between silence and the word of language. First, there is silence as the withdrawal of the word. This is the silence of the voice that wants to hold back from speaking and to hold back the word from falling into chatter. Second, there is silence as giving voice to words. This is the silence that enacts the spacing within language that possibilizes the intentions of meaning within speech and the efforts of communicative understanding. Third, there is silence as the beginning of the word. This is the silence that stands in relation to a hidden or absent origin, such as an unknown god, generating thereby a word from silence that is a beginning.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".