« ¡Un clásico que nunca falla ! ». Ingeniería lingüística y degustación : la invención como estrategia psicosociodinámica en el trinomio alemán-inglés-español (DE-EN-ES)
Bibliographic record
Abstract
A Timeless Classic! Linguistic Engineering and Gastronomy: Invention as a Psychosociodynamic Strategy in the German-English-Spanish Trinomial (DE-EN-ES)
 The aviation industry has always been recognized for its focus on the customer experience, and airline menus are a faithful reflection of this commitment. This study highlights that these menus not only offer a wide variety of gastronomic options but also employ linguistic engineering techniques to persuade and entice passengers into making specific choices. By analyzing the menus of Lufthansa, Air Canada, and Iberia on the PUJ- YYZ-AMS-FRA route in December 2022, some of the most common strategies used in linguistic engineering were identified. For instance, German menus from Lufthansa are characterized by their rigor in nominalization and brand exposure, suggesting that the company uses food as a means to promote its brand and differentiate itself from competitors. On the other hand, Air Canada's English menus deliberately use adjectives to convince customers to choose specific options. The same technique applies in the Spanish text of Iberia, where inventions and exclamations are used to tempt passengers into selecting specific culinary choices. It is interesting to note how these strategies are premeditated and strategically applied in the linguistic engineering of airline menus. The way this type of persuasion is linguistically expressed also varies according to the language, suggesting that airlines adapt their techniques to the cultures and linguistic expectations of their customers. Ultimately, this study emphasizes that linguistic engineering is a powerful tool for companies seeking to persuade customers and enhance their overall experience. While this work focused on the menus of the mentioned airlines at a specific moment, similar dynamics are likely to be found in other texts and diverse media. Therefore, linguistic engineering provides an opportunity for companies to develop more effective persuasion and marketing strategies. The application of these strategies can not only improve original texts but also be valuable in the field of transcreation, a highly functional branch in language translation and interpretation. These tacit strategies provide insights that can be considered as an autonomous method to enhance communication and customer satisfaction.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".