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Record W4416366212 · doi:10.22329/jtl.v19i5.10276

A History of Applied Linguistics From 1980 to the present

2025· article· en· W4416366212 on OpenAlexvenueno aff
Joko Slamet

Bibliographic record

VenueJournal of Teaching and Learning · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsApplied linguisticsNeurolinguisticsSection (typography)Quantitative linguisticsCitationHistorical linguisticsOn LanguageTheoretical linguisticsCorpus linguistics

Abstract

fetched live from OpenAlex

Kees de Bot’s “A History of Applied Linguistics: From 1980 to the Present”, published by Routledge in 2015, is a seminal work that meticulously traces the trajectory of applied linguistics over the past few decades. The book spans 11 chapters over approximately 168 pages, offering a detailed exploration of Applied Linguistics (AL). De Bot begins by analyzing the diverse informants who have shaped AL, considering factors like gender, race, educational backgrounds, and affiliations. He critically examines AL’s definitions, its autonomy, and its relationships with fields like TESOL and AILA. Profiles of influential leaders highlight their contributions, while a thorough review covers seminal articles and books, emphasizing publishers’ roles in research dissemination. The book explores theoretical and methodological trends, including corpus linguistics, discourse analysis, and new areas like neurolinguistics and technology in language learning. De Bot discusses psycholinguistic and sociolinguistic dimensions such as language acquisition, identity, multilingualism, and language policy. His exploration of Complex Dynamic Systems Theory (CDST) applies it to understanding language dynamics and individual differences. A citation analysis section examines publication impact and academic influence dynamics. Ultimately, De Bot reflects on AL’s broad impact on language education, from theoretical insights to practical applications.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.022
GPT teacher head0.250
Teacher spread0.228 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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