Forum on “The emotional landscape of English medium instruction (EMI) in higher education”
Bibliographic record
Abstract
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. Other Information Published in: Linguistics and Education License: http://creativecommons.org/licenses/by/4.0/ See article on publisher's website: https://dx.doi.org/10.1016/j.linged.2023.101181
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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".