Bibliographic record
Abstract
This series of textbooks addresses a range of topics taught within TESOL programs around the world.Each volume is designed to match a taught 'core' or 'option' course (identified by a survey of TESOL programs worldwide) and could be adopted as a prescribed text.Other series and books have been aimed at Applied Linguistics students or language teachers in general, but this aims more specifically at students of ELT (English Language Teaching -the process of enabling the learning of English), with or without teaching experience.The series is intended primarily for college and university students at third or fourth year undergraduate level, graduates (pre-service or in-service) studying TESOL on Masters programs, and possibly some TESOL EdDs or Structured PhDs, all of whom need an introduction to the topics for their taught courses.It is also very suitable for new professionals and people starting out on a PhD, who could use the volumes for self-study.The readership level is introductory and the tone and approach of the volumes will appeal to both undergraduates and postgraduates.This series answers a need for volumes with a special focus on intercultural awareness.It is aimed at programs in countries where English is not the mother tongue, and in English-speaking countries where the majority of students come from countries where English is not the mother tongue, typical of TESOL programs in the UK and Ireland, Canada and the US, Australia and New Zealand.This means that it takes into account physical and economic conditions in ELT classrooms around the world and a variety of socio-educational backgrounds.Each volume contains a number of tasks which include examples from classrooms around the world, encourage comparisons across cultures, and address issues that apply to each student's home context.Closely related to the intercultural awareness focus is a minor theme that runs throughout the series, and that is language analysis and description, and its applications to ELT.Intercultural awareness is indeed a complex concept and we aim to address it in a number of different ways.Taking examples from different cultural contexts is one way, but the volumes in the series also look at many other educationally relevant cultural dimensions such as sociolinguistic influences, gender issues, various learning traditions (e.g.collectivist vs individualistic), and culturally determined language dimensions (e.g.politeness conventions).
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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 teacher head, 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".