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
In this paper, we examine the role of discourse relations (relations between propositions) in the interpretation of evaluative or opinion words. Through a combination of Rhetorical Structure Theory (or RST; Mann and Thompson, 1988 ) and Appraisal Theory ( Martin and White, 2005 ), we analyse how different discourse relations modify the evaluative content of opinion words, and what impact the nucleus–satellite structure in RST has on the evaluation. We conduct a corpus study, examining and annotating over 3,000 evaluative words in fifty movie reviews in the SFU Review Corpus ( Taboada, 2008 ) with respect to five parameters: word category (noun, verb, adjective or adverb), prior polarity (positive, negative or neutral), RST structure (both nucleus–satellite status and relation type) and change of polarity as a result of being part of a discourse relation (Intensify, Downtone, Reversal or No Change). Results show that relations such as Concession, Elaboration, Evaluation, Evidence and Restatement most frequently intensify the polarity of opinion words, although the majority of evaluative words do not undergo changes in their polarity related to the type of relation that they are a part of. We also find that most opinion words (about 70 percent) are positioned in the nucleus, confirming a hypothesis based on the literature that nuclei are the most important units when extracting opinion automatically.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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