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Record W7043023550

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 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Repository of the University of El Salvador (University of El Salvador) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsForeign languageStress (linguistics)Reading (process)GlobalizationAmerican EnglishOrder (exchange)EntertainmentImmigrationLatin Americans
DOInot available

Abstract

fetched live from 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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.004
GPT teacher head0.193
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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