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Record W2618951977 · doi:10.5539/ass.v13n6p181

The Job Analysis of Teaching Secretaries Working in an International Environment --- Taking Jinan University, Guangzhou, China as the Research Paradigm

2017· article· en· W2618951977 on OpenAlexvenueno aff
Jia Li

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsChinaQuality (philosophy)Working environmentWork (physics)Teaching staffPsychologyMedical educationPedagogyMathematics educationPolitical scienceSociologyMedicineEngineering

Abstract

fetched live from OpenAlex

Under the guidance of national policy, Chinese higher education institutions use English as teaching language. With this feature, an international environment is formed that the university offers places to eligible students from all over the world and employs qualified teachers from all over the world to teach students. Working in an international environment, the teaching secretaries’ job is different. This paper analyzes the job particularity of teaching secretaries working in an international environment in higher education institutions that their working language is English and they have to work with teachers and students from all over the world, answer teachers and students questions and solve their problems related to teaching affairs. This paper proposes some effective measures from the personal aspect and university aspect to raise the quality of the teaching secretaries’ job. And this paper gets a conclusion that the teaching secretaries working in an international environment should increasingly improve their comprehensive quality, professional knowledge, and management skills.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.002
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.067
GPT teacher head0.336
Teacher spread0.269 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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