Emotion labor, investment, and volunteer teachers in heritage language education
Bibliographic record
Abstract
Abstract Studies informed by poststructuralist theories of language have examined the relationship between language teachers’ emotion labor, identity, and agency. However, research has not yet explored the relationship between emotion labor and volunteer teaching, which is an important practice in language education. Our research seeks to address this gap, drawing on a 2‐year qualitative case study at the community‐based Vancouver Bangla School (VBS). With emotion labor and investment as the conceptual underpinnings, our study investigated how the VBS heritage language (HL) program structured the emotion labor of seven volunteer teachers, what the feeling rules associated with the VBS program were, and the extent to which volunteer teachers’ investment in HL education helped them manage their emotion labor. Data sources included participant classroom observations, field notes, focus group and interview transcripts, questionnaires, and educational resources, which were analyzed using reflexive thematic analysis. Findings indicate that the emotion labor of volunteer teachers was structured by the following characteristics of the VBS program: lack of funding, poor organizational structure and teacher recognition, challenges of online teaching, insufficient number of teachers, limited parental support, and lack of training. This emotion labor was associated with four feeling rules implicit in the VBS program: (a) be generous and caring, (b) be committed and dedicated, (c) be a good and efficient teacher, and (d) have limited expectations of the community. Findings suggest that teachers’ investment in Bangla as a mother tongue in multicultural Canada, and their investment in promoting the children's transcultural identities, was particularly powerful, and enabled the volunteer teachers to navigate and manage their emotion labor. The study suggests that an enhanced understanding of a language teacher's investment in a program, institution, or community might provide insight into the important relationship among desire, agency, and emotion labor.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".