Bibliographic record
Abstract
This article presents a literature review of selected studies devoted to the research of the features of learning spaces and their impact on the development of language competencies of teachers and students. The presented review is a part of a comprehensive project to develop a conceptual framework and conduct empirical research based on “English Only Space” (EOS) – an innovative learning space implemented at Atyrau State University. When designing EOS, the author used an approach that defines, under the learning space, or, according to the OECD Concept (2013), the physical environment of learning, “physical spaces (including formal and non-formal) in which teachers and students interact, content (content), equipment and technology.” Practical decisions in designing this learning space were based on a detailed analysis of the latest publications from different disciplines, comparing various authors’ views and determining new trends in a number of fields such as philology, applied linguistics, pedagogy, psychology, and ecology. Such approach allowed achieving conditions when language teaching and learning turns into an instrument of interdisciplinary cognition, and the physical environment provides affordances formanaging resources efficiently in order to achieve maximum learning outcome. At the same time, the present literature review served as a driver for an active start for further practice-oriented research in EOS based on two assumptions: the need to search and develop unique ways of learning English, exploring a wide range of influencing (environmental) factors; the fulfillment of the main purpose of the space if it promotes and supports experiences that promote learning and the achievement of learning outcomes.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".