Socializing Immigrant Jobseekers: The Role of Pre-Employment Mentoring Programs
Bibliographic record
Abstract
Mentoring is a developmental relationship between a senior, experienced, knowledgeable mentor and a less experienced protégé for career and psychosocial development. The benefits of mentoring in the workplace setting are well documented. Organizations increasingly use formal mentoring programs to socialize new employees, retain existing workforce, and increase workplace diversity. This thesis investigates mentoring at the pre-employment stage, where formal mentoring programs are used as support interventions to assist immigrants in finding employment. Facilitated by immigrant-serving organizations (ISOs), sometimes in partnerships with employing organizations, pre-employment mentoring programs recruit volunteer mentors and match them with immigrant protégés in need of employment support. Drawing on surveys, archival data, and semi-structured interviews with 15 immigrants and 11 ISO representatives in Ontario, Canada, this thesis provides qualitative insights into 1) the role of mentors in socializing immigrants at the pre-employment stage (i.e., when immigrants go through anticipatory socialization); and 2) the promises and pitfalls of pre-employment mentoring programs. The findings suggest that mentors played the role of socialization agents by facilitating immigrants’ learning about the labour market, profession, and employing organizations of interest while providing critical emotional support to prepare immigrants for conducting effective job searches (i.e., job search readiness). However, job search readiness did not imply a successful transition to employment, as many immigrants still struggled to enter organizations upon completion of pre-employment mentoring programs. A critical examination of the programs identified the programs’ benefits for various stakeholders (e.g., immigrants, mentors, ISOs, and employers) and challenges concerning mentor-protégé matching, mentor-protégé commitment to the mentoring relationship, and unmet expectations. The program’s reliance on volunteering and stakeholders’ varying understandings and expectations of mentoring contributed to these challenges, resulting in inconsistency in immigrants’ perceived effectiveness of mentoring. This thesis contributes to a better understanding of immigrant socialization by unveiling the complexity associated with anticipatory socialization and the potential role of mentors in facilitating such a process. By focusing on pre-employment mentoring programs organized by ISOs, the thesis generates new insights into formal mentoring programs and highlights the role of ISOs in immigrant labour market integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".