More Than a Teacher: Understanding the Teacher-Learner Relationship in a Public High-School in South Africa
Bibliographic record
Abstract
Teaching is considered a caring occupation due to the nature of the interaction between teachers and learners (Hocschild, 1983). Care giving can be a demanding task, however, emotional labour invested in the occupation – with regards to their relationships with learners – contributes to job satisfaction, commitment and be emotionally rewarding. The proximity or distance of these relationships are dependent on five emotional geographies, namely socio-cultural, moral, professional, physical, and political relatability (Hargreaves, 2001). This paper draws on a case study of teachers at a former Model C high-school in South Africa to examine the formation and development of relationships formed between the teacher and learner. The article suggests that teachers adopt three additional roles outside that of teaching. These roles, the coach, counsellor, and parental figure, foster emotional understanding (Denzin, 1984) between the teacher and learners, which creates a positive classroom climate. These roles are deemed necessary for the fulfilment of successful relationships with learners. However, there are challenges which teachers face when attempting to develop these bonds with learners, which include a negative classroom climate, socio-cultural distance, and sexual harassment – faced by women teachers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".