Lexical Innovation in Ecotourism Discourse: The Case of Eco(-)lodge
Bibliographic record
Abstract
As repositories of the cultures whose language they describe, lexicographical resources partake in the (re)production of dominant ideologies. This is especially relevant with regard to the current ecological crisis. With this in mind, the present article contributes to research within the field of ecolexicography. Combining critical lexicography with ecolinguistics, it acknowledges the role of lexicographical resources in shaping the users’ awareness of environmental protection. In particular, this study investigates lexical innovation within ecotourism discourse in order to understand whether “ecotourism talk” can respond to its sustainable objectives. The research focusses on one specific instance, the noun eco(-)lodge, which is examined by searching both native speakers’ and learners’ dictionaries and specialised and general English corpora. Results highlight a partial clash between the two types of sources. While examples of usage mostly connote ecolodges as a type of luxury and exclusive accommodation placed in natural—i.e., non-urban—contexts, dictionaries define them solely with reference to their supposed minimal environmental impact. Outcomes suggest a semantic bleaching of the combining form eco- in ecotourism discourse, which is exploited in lexical creations to advertise a form of niche tourism that does not always align with ecological concerns.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".