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Record W24060158 · doi:10.1037/a0034350

ANALISA KURIKULUM PELAJARAN BAHASA MANDARIN TINGKAT SD SEKOLAH NASIONAL PLUS DI DAERAH PLUIT DAN MUARA KARANG (STUDI KASUS : SEKOLAH NASIONAL PLUS DIAKONIA DAN SEKOLAH PERMAI PLUS)

2007· dissertation· en· W24060158 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHumanitiesMandarin ChinesePolitical sciencePhilosophyLinguistics

Abstract

fetched live from OpenAlex

Incivility between customers and employees is common in many service organizations. These encounters can have negative outcomes for employees, customers, and the organization. To date, researchers have tended to study incivility as an aggregated and accumulated phenomenon (entity perspective). In the present study, we examined incivility as it occurs during a specific service encounter (event perspective) alongside the entity perspective. Using a mixed-method multilevel field study of customer service interactions, we show that individual customer incivility encounters (i.e., events) trigger employee incivility as a function of the employee's overall accumulated impression of the (in)civility in his or her customer interactions, such that the effects are more pronounced among employees who generally perceive their customer interactions to be more versus less civil. We also find that these interactive effects occur only among employees who are lower (vs. higher) in negative affectivity. Our results show that, in order to expand the understanding of customer incivility, it is important to study the incivility encounter, the social context in which negative customer interactions occur, and individual differences.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0050.001
Research integrity0.0020.005
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 head0.016
GPT teacher head0.299
Teacher spread0.284 · 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