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Record W4390958019 · doi:10.37213/cjal.2023.32985

Bienvenidos A Bordo: From Task-Based Needs Analysis to Design: Spanish-Destination Flight Attendants

2023· article· en· W4390958019 on OpenAlexvenueno aff
Shakira Keller, Roger Gilabert

Bibliographic record

VenueCanadian Journal of Applied Linguistics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
FundersUniversitat de BarcelonaGeneralitat de Catalunya
KeywordsTask (project management)Task analysisTriangulationComputer scienceInformation needsHuman–computer interactionWorld Wide WebEngineeringGeography

Abstract

fetched live from OpenAlex

The aim of this task-based needs analysis is two-fold: firstly, to uncover the tasks performed by U.S.-based Spanish-language flight attendants and the associated language needs and, in doing so, to expand the breadth of task-based needs analysis (TBNA) through the application of multiple methods and sources (Long, 2005) and tackling the under-researched issue of transfer from TBNA to task design (Gilabert & Malica, 2021a; 2021b). A questionnaire-guided interview and online survey were used. Analysis of the extracted information illuminated the essential tasks and subtasks (Gilabert, 2005), including details regarding frequency, need for training, and language use. Findings suggest that each task and subtask requires varying amounts of Spanish, as well as knowledge of distinct linguistic dimensions. Triangulation of multiple sources and methods adds to the understanding of the tasks and language needs. Finally, suggestions as to how the outcome of this NA may transfer to task design are presented, hence extending the field of TBNA.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.387
Teacher spread0.312 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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