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Record W2886477612 · doi:10.5539/elt.v11n9p41

The Need Analysis of Maritime English Learning Materials for Nautical Students of Maritime Academy in Indonesia Based on STCW’2010 Curriculum

2018· article· en· W2886477612 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)CurriculumNonprobability samplingPsychologySurvey researchEngineeringMathematics educationMedical educationPedagogySociologyApplied psychologyMechanical engineeringMedicine

Abstract

fetched live from OpenAlex

This study was aimed to investigate the Maritime English learning materials needed by the students of nautical department of Maritime Academy in Indonesia based in STCW’2010 curriculum. The participants of the research were the seafarers, the port authorities, the shipping business employees, the English lecturers, and the cadets of Maritime Academy. They were totally about 48 participants. They were selected by using purposive random sampling technique. There were three types of instruments for collecting data those were questionaire, documentary sheet, and interview. The results of the study shows that 1) there were three basic leaning materials categories of Maritime English namely a) the nautical contents, b) language skills, and c) languistic features and 2) the level of needs of those materials were different from categories to another categories and within categories.

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.

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)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.007
GPT teacher head0.280
Teacher spread0.273 · 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