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Record W4226079186 · doi:10.5430/wjel.v12n3p157

Literature Enhances Communication Skills: A Comprehensive Review

2022· review· en· W4226079186 on OpenAlexvenueno aff
M. Sharma, Manita Devi, Rajesh Dangoria, Vibhor Jain

Bibliographic record

VenueWorld Journal of English Language · 2022
Typereview
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)PragmaticsCompetence (human resources)Computer scienceKey (lock)Communicative competenceMeaning (existential)Communication skillsLinguisticsPsychologyPedagogyArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

Many non-native and native speakers all around the world have proven that English is an international language. It is sometimes referred to as the global language since it is the primary means of communication across countries. In our nation, the usage of English as a common language plays an essential role in education. English learners must place a strong emphasis on the use of communication. The ultimate goal of language instruction is to improve communication skills. Pragmatics is the method in which meaning is transferred through communicating, and linguistic skill is regarded as an instrument for communication. Pragmatic competence is the capacity to comprehend and communicate meanings that are more correct and acceptable for the cultural and social contexts in which communication takes place. Even though English is used at many levels of communication, the speakers must be familiar with a variety of pragmatic components to create coherency and the capacity to react in a variety of scenarios. As a result, one of the key aims in the field of education should be the development of pragmatic ability and also help for future understanding.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.019
GPT teacher head0.311
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2022
Admission routes1
Has abstractyes

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