The Presumptions of Meaning. Hamblin and Equivocation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When we use a word, we face a crucial epistemic gap: we ground our move on the fact that our interlocutor knows the meaning of the word we used, and therefore he can interpret our dialogical intention. However, how is it possible to know the other’s mind? Hamblin explained this dialogical problem advancing the idea of dialectical meaning: on his view, the use of a word is based on a set of presumptions. Building on this approach, the use of a word in a dialogue can be analyzed in terms of presumptive reasoning, while the manipulative strategies based on slanted or loaded terms or redefinitions can be conceived as forms of conflicts of presumptions. A presumptive approach to meaning can also ground different dialectical strategies to solve misunderstanding or definitional disagreements, or tactics to undermine the interlocutor’s arguments by advancing charges of equivocation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it