Bibliographic record
Abstract
I propose that computational evaluation is an emerging field of research, one that applies computational techniques to the representation and processing of evaluative language, associating evaluative meanings with expressions of human language. The study of evaluative language has a long history in linguistics, encompassing research on attitude, subjectivity, point of view, and evidentiality, with more recent studies on appraisal or emotion language. At the same time, computational linguistics has by now accumulated a back catalogue of research going back a couple of decades into how we can extract evaluation, sentiment, and opinion automatically from text. I briefly survey this history, to then outline a proposal that the study of evaluative language from a computational point of view crosscuts all levels of language, from morphology and the lexicon to figuration, and requires a comprehensive understanding of language. By way of illustration, I will discuss research on appraisal, abusive language online, and the use of metaphors in the expression of negative opinion. This work has applications in content moderation, detection of misinformation, or information retrieval, but it is also interesting in its own right, as a theoretical field in linguistics and computational linguistics.
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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.010 |
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".