Emotions as entanglements: unpacking teachers’ emotion management and policy negotiation in English-medium instruction programmes
Bibliographic record
Abstract
There is a dearth of knowledge on the emotional challenges content-area teachers in English-medium instruction (EMI) programmes face, and how they manage their emotions in their efforts to negotiate a top-down language policy. This paper examines the entangled emotional experiences of EMI content-area teachers in Nepal’s school education. In contrast to a psychological approach to teachers’ emotions, this article draws on sociocultural and ideological perspectives on emotions to unpack a connection between emotions, institutional language policies, language ideologies, identity, and teacher agency. The analysis of EMI teachers’ emotional dynamics essentially identifies their emotions as ‘entanglements’, reflecting the interconnectedness of emotions with other variables such as language ideology, identity, and agency in content-and-language-integrated education. The findings of this study showed that teachers’ limited English proficiency led to negative emotions (e.g. anxiety, fear, frustration, and shame), stimulating them to use English-Nepali bilingualism as a creative strategy to manage their emotional challenges and also to exercise their agency in response to their students’ needs. However, their translanguaging strategy – which otherwise might have included the students’ home language, Bhojpuri – was restricted by hegemonic language ideologies. The findings show that multilingual teachers typically do not experience emotions in a vacuum but in response to other social phenomena. The paper supports the argument that teacher emotion management is not an apolitical process but is rather ideologically and discursively constructed and situated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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 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".