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Record W2795457962 · doi:10.11575/prism/31768

Cultural Influences on Impression Management: A Focus on Internationally-Educated Engineers

2018· dissertation· en· W2795457962 on OpenAlexaboutno aff
Jelena Radan

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsImpression managementFocus (optics)ImpressionEngineering ethicsEngineeringPsychologyPolitical scienceSociologyComputer scienceSocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Internationally-educated immigrants in regulated professions often encounter numerous barriers when striving to find employment commensurate with their skills and experience in Canada. Understanding the Canadian cultural norms and workplace culture is imperative for their success in the employment interview. This research captures employers' perceptions of how cultural influences in the interview impact their evaluations of job applicants, and in turn, their hiring decisions. To expand upon the existing research, the present study sought to explore how cultural differences within the interview context influence Human Resources professionals' perceptions of candidates and in turn, how these perceptions affect Human Resources professionals' evaluations of job applicants. Through the use of the Enhanced Critical Incident Technique, eight Human Resources professionals within the Oil and Gas Industry offered incidents that facilitated, impeded, and could improve the interview performance of Internationally-Educated Engineers. Five overarching themes emerged from the data analysis: (a) industry-specific knowledge requirement, (b) confidence and abilities, (c) personal attributes, (d) job-search process, and (e) communication skills. These results were considered in light of relevant literature, including recommendations for employers and career practitioners to enhance employment outcomes of skilled immigrants and directions for future research.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2018
Admission routes1
Has abstractyes

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