Forum on “The emotional landscape of English medium instruction (EMI) in higher education”
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
<p dir="ltr">The studies presented in this special issue on the emotional landscape of English medium instruction (EMI) in higher education settings offer valuable insights into the variety of emotions that get entangled in policies, discourses, and practices in local EMI contexts, and the emotional effects of EMI on various stakeholders, such as students, teachers, and administrators. It is also important to contemplate 1) how the research findings can be applied in EMI higher education settings in order to develop more emotionally supportive and socially just (De Costa et al., 2021) EMI environments and 2) how to move forward with the research agenda on emotions and EMI. With these questions in mind, the contributors to the special issue were asked to review one another's studies and briefly respond to the prompt listed below. The prompt was created and the responses organized and edited by Sara Hillman and Wendy Li. The authorship order for this piece was based on the order in which the editors arranged the contributors' responses. <h2>Other Information</h2><p dir="ltr">Published in: Linguistics and Education<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.linged.2023.101181" target="_blank">https://dx.doi.org/10.1016/j.linged.2023.101181</a>
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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