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
This paper briefly introduces several of the aspects to take into account in order to properly describe and analyze the expression of uncertainty in textual data. Different types of ambiguity inherent to the nature of language itself are presented. Linguistic ambiguities can be observed between symbols and the meanings arbitrarily attached to them. Many natural language processing techniques can be applied to texts to minimize linguistic ambiguities. Referential ambiguities relate to the world and can be observed through extra-linguistic environments, each potentially impacting the interpretation of natural language utterance. From a linguistic point of view, the identification and automatic tagging of expressions of certainty/uncertainty in textual data is a sine qua non condition to enable the empirical study and modeling of how humans assess certainty through their use of language. Such analysis is required to generate future language-dependent models of certainty/uncertainty suitable for information fusion systems.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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