MétaCan
Menu
Back to cohort
Record W2745698435 · doi:10.5296/ijl.v9i4.11701

Sociolinguistic Perceptions of Tú, Usted and Vos in the Highlands of Ecuador

2017· article· en· W2745698435 on OpenAlexaff
Jesús Toapanta

Bibliographic record

VenueInternational Journal of Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClosenessFormalityPronounFriendshipPsychologyLinguisticsPerceptionReciprocalMeaning (existential)Social psychologyMathematics

Abstract

fetched live from OpenAlex

The Spanish pronouns ‘Tú’, ‘Usted’ and ‘Vos’ all translate into the English second person singular ‘You’. However, this does not mean that they convey the same meaning. In Ecuador, for example, the pronoun Usted is normally used to signal formality, distance, and not familiarity; and, Tú and Vos are often used to signal friendship, closeness, and informality. In addition, these pronouns adopt different meanings depending on where in Ecuador the interaction takes place. For example, in a reciprocal relationship between classmates, Vos implies friendship and closeness in the city of Cuenca, but in the city of Quito Vos implies lack of respect. In this sense, this study examines how college students perceive the usage of these pronouns at home, at the university, and at the workplace. This paper analyses samples taken from three cities in the highlands—Quito, Cuenca, and Loja—and describes how these pronouns are being used at these locations in Ecuador. The results reveal this very peculiar variation and show the different meanings and tendencies of these pronouns.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.318
Teacher spread0.286 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of LinguisticsSame topicSpanish Linguistics and Language StudiesFrench-language works237,207