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Record W4319601135 · doi:10.54097/ehss.v8i.4496

Emotional Labour and Misogyny: a general look at gender differences among teachers in Chinese high schools

2023· article· en· W4319601135 on OpenAlexaff
Shihui Li, Xiyu Liu, Xincan Wang, Yinshen Zhao

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmotional laborMainland ChinaChinaContext (archaeology)PsychologyMainlandQuality (philosophy)Association (psychology)Developmental psychologySocial psychologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Emotional labour and misogyny have been studied in the gender field for decades. Numerous studies focused on the association of the two subjects and education industry in the western world respectively, while little research of such topics has been done in the eastern world context. This study took a step to examine the gender differences of high school teachers’ emotional labour in mainland China by reviewing previous literature and conducting interviews with teachers and students at a school located southeast China. The result found that misogyny is an essential factor causing different emotional labour contents between genders. Yet the consequences of this difference can be both positive and negative due to the varying types of emotional labour, and the detailed impacts are discussed. This study results also suggested that improvements of gender equality in classrooms, especially regarding teachers’ emotional labour, are crucial and urgent to be made for both teachers’ well-beings and students’ learning quality.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.374
Teacher spread0.296 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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