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Record W7083329647 · doi:10.1108/edi-02-2025-0093

Mentoring immigrant job seekers: a socialization perspective

2025· article· en· W7083329647 on OpenAlexaff

Bibliographic record

VenueEquality Diversity and Inclusion An International Journal · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsCarleton UniversityThompson Rivers University
Fundersnot available
KeywordsSocializationImmigrationPerspective (graphical)Context (archaeology)Process (computing)Qualitative research

Abstract

fetched live from OpenAlex

Purpose While mentoring is an important tool for employee development in organizations, its potential for integrating immigrants at the pre-employment stage remains underexplored. This paper examines the role of mentors in immigrants’ anticipatory socialization. Design/methodology/approach We followed an inductive, qualitative approach to investigate immigrants’ experiences of pre-employment mentoring facilitated by immigrant-serving organizations (ISOs). We drew on semi-structured interviews with immigrants and staff members of ISOs and archival data to understand the context and protégé experiences. Findings Mentors increased immigrants’ job search readiness by socializing them into the labor market, professions and organizations while supporting their emotional coping. Immigrant socialization emerges as a complex, multifaceted process requiring learning knowledge, skills and behaviors at multiple levels. Originality/value The study demonstrates the interrelatedness of mentoring and organizational socialization by elaborating on the process by which mentors support the anticipatory socialization of immigrant job seekers. Additionally, it unpacks the process of immigrant socialization by identifying specific knowledge, skills and behaviors that immigrants acquired through mentoring in order to meet societal, professional and organizational expectations.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.001
Open science0.0010.010
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 head0.115
GPT teacher head0.459
Teacher spread0.344 · 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 designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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