A Corpus-Based Approach to Analysing Evaluative Adjectives in Student-Generated Tourism Texts
Bibliographic record
Abstract
This study explores the potential of integrating corpus-based analysis of evaluative adjectives into foreign language instruction. Research has indicated that exposure to authentic corpus data can reinforce language proficiency and promote precise adjective usage across various communicative contexts. To investigate these claims, 60 first-year translation and interpreting university students at a B2 proficiency level participated in the study. They were tasked with selecting and applying adjectives to describe Gran Canaria, emphasizing lexical choices that capture the island’s identity within a tourism-related context. Our research employed a mixed-methods design, combining quantitative frequency analysis with qualitative assessments to reveal patterns in adjective usage and measure their practical utility. The findings indicate that a corpus-driven approach significantly enhances learners’ awareness of adjective classifications and their ability to use adjectives in nuanced ways across different registers. 
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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 | 0.001 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".