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Record W2942288589 · doi:10.5539/jedp.v9n1p77

More Than a Teacher: Understanding the Teacher-Learner Relationship in a Public High-School in South Africa

2019· article· en· W2942288589 on OpenAlexvenueno aff
Leza Soldaat

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersAndrew W. Mellon Foundation
KeywordsPsychologyHarassmentClassroom climateFace (sociological concept)PoliticsSocial psychologyPedagogySociologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.217
GPT teacher head0.399
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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