Bibliographic record
Abstract
This paper reports on part of the research on evaluative language currently carried out within the CONTRANOT project,[lxxviii] which aims at the creation and validation of contrastive functional descriptions through corpus analysis and annotation in English and Spanish. More concretely, we will present the coding scheme designed for Attitude, a subcategory of Appraisal as studied within Systemic-Functional Linguistics (Martin and White, 2005; White, 2003). The criteria for selection and annotation of spans of Attitude in the coding scheme are specified and illustrated with examples from the Simon Fraser University Review Corpus (Taboada, 2008), a corpus of consumer-generated reviews on hotels, books and movies, and a small-scale English-Spanish contrastive analysis of these reviews has been carried out. The scheme is to be used for the future annotation of evaluation in an English-Spanish corpus, CONTRASTES (Lavid, 2008; Lavid et al., 2007, 2010). Once annotated, the reviews will be part of this corpus.
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 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 | 0.003 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".