Impact of Guanxi Cultural Background of ACT Graduates on the Outcomes of the Job-Search Process
Bibliographic record
Abstract
How using social capital can help job seekers to get a job has been extensively discussed by literature. There is consensus in literature that social networks operate differently across different cultures. While in relations-centred China an interdependent framework in networks is created, personal networking activities in Western cultures are characterised by individualism. Using quantitative data of personal networks of university graduates of a joint international graduate program, the Austrian-Canada-Taiwan (ACT) program, this thesis analyses the following research questions: Which effect does guanxi of the Asian culture have on job-search outcomes of ACT graduates in comparison to Western graduates? Does guanxi have an impact on the job quality of Asian ACT graduates compared to Western graduates? In order to effectively evaluate these questions logistic binary regression analyses are conducted with data generated from an online questionnaire. Findings do not suggest a significant correlation between nationality groups and their job-search outcomes not implying there is an effect of the cultural background of a job seeker on the success of obtaining an interview or receiving a job offer. The self-established quality ratio based on the salary of participants shows that ACT graduates with assumed guanxi cultural background receive lesser quality jobs than their Western counterparts. Despite their cultural heritage, this study concludes that managing a team of people in their first job after graduation is not an important job characteristic of ACT graduates whereas working in a company which operates in multiple countries is.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".