Review of Melo-Pfeifer (2023): Linguistic Landscapes in Language and Teacher Education. Multilingual Teaching and Learning Inside and Beyond the Classroom
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.
Bibliographic record
Abstract
The edited volume by Slvia Melo-Pfeifer offers an international account of how linguistic landscapes (LLs, which refer to the use of language in the environment such as the words and images displayed in public spaces) can be used to promote multilingual pedagogies in diverse settings around the world, both inside and beyond the classroom, in (foreign) language learning and teacher education.The book draws from empirical case studies conducted by the collaborators of the Local Linguistic Landscapes for Global Language Education in the School Context (LoCALL) project. 1 In the introduction of the volume (Chapter 1), Melo-Pfeifer discusses LLs as an educational research field that has the potential to be used as a research tool and data source to address current issues and challenges in multilingualism, specifically in relation to language education.Melo-Pfeifer's stance is that LLs exist at the intersection of three turns in applied language studies: (1) the multilingual turn, which includes the implementation of multilingual pedagogies not only in the language classroom but across the curriculum, as well as a research agenda focused on (linguistic) justice in education; (2) the visual turn, which is reflected in the growing disciplinary interest in students' development of linguistic and cultural awareness, aesthetic competence, and visual literacy; and (3) the spatial turn, where meaning is constructed and emerges in context, in a given spatial orientation, depending on individuals' spatial repertoires.Additionally, this chapter presents an overview of all chapters, with the chapters grouped in four parts, each of which addresses a different dimension of LLs in relation to multilingual education.Part 1 (Chapters 2-5) explores the role of LLs as multilingual pedagogical resources in order to establish a connection between the school and the community and to empower students as active learners and decision-makers of their
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it