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The role of the Spanish vowel system as a key factor in the accent of the English spoken by undergraduate students in their fourth and fifth academic year of the Foreign Language Department at the University of El Salvador, main campus, year 2017

2018· dissertation· en· W7043023550 sur OpenAlexaboutno aff

Notice bibliographique

RevueInstitutional Repository of the University of El Salvador (University of El Salvador) · 2018
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueLand Use and Ecosystem Services
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésForeign languageStress (linguistics)Reading (process)GlobalizationAmerican EnglishOrder (exchange)EntertainmentImmigrationLatin Americans
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Globalization and English as the dominant language in the world have led most of the countries to adopt it as a requirement to be competitive in science, international trade, technology, the arts, etc. Blommaert (2010) observed that English language is intrinsically connected to the process of globalization; thus, the fact that two Anglophone nations, England and the United States of America, have played a significant role within the five continents throughout the last four centuries is the reason why English is the primary option when it comes to learning a foreign language. Furthermore, professionals from different areas have to learn English in order to keep their knowledge updated. Additionally, learning English grants more job opportunities and a wider range of choices for entertainment regarding music, movies, video games and literature. Thus, mastering the skills of speaking, listening, reading and writing English guarantees a diverse sort of advantages to people and those rewards motivate to overcome different difficulties and face challenges that might restraint them to reach the required proficiency level to communicate effectively in that foreign language. The constantly growing community of Latin Americans in the territory of the United States of America implies a considerable endeavor for the institutions of the host country and for the new comers who are demanded to be skillful in English in order to achieve their full integration. In addition to this, many Canadian and American companies have opened branches in the south of the American continent due to the free trade agreements they signed with countries of that region, which involves that Spanish speaking countries necessitate people who master English as a condition to take advantage of the new business and job opportunities. Also, the tourist industry is already a very important sector in the economy of several countries of Central and South America which means that all employees involved in that area have to speak English when dealing with tourists who are Anglophone. Consequently, native Spanish speaker population is in need to learn English either as a second or foreign language. Governments are implementing English language policies grounded partly in an economic rationale, propelled by a focus on building the proficiency of the population in part to boost a country’s competitiveness in a globally integrated market (British Council, 2017). Consequently, the number of native Spanish speakers who become Spanish- English bilinguals is expanding as a precondition to come up to the new challenges. Being an English speaker in El Salvador is an advantage when applying for a job. The call center industry has been permanently growing in El Salvador and the companies complete their staff by hiring Salvadorans who have studied English as a foreign language and those who learned it while living abroad. In addition to that, Salvadoran professionals are required English knowledge either to get a new job or to be promoted to a better position at their current job. This sort of boom of English language in the national territory has generated a bloom of language schools which demand teachers with native-like proficiency or at least a high level in English. Hence, mastering English language widens the range of job opportunities for Salvadoran population, yet people have to overcome some conditions that hinder to meet the requirements in regards to the proficiency level required by employers

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,989

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0040,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,193
Écart entre enseignants0,188 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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