Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".