A Novel Framework for Investigating Immigrant Experiences in Cybersecurity – Integrating Human Capital Theory with Equity, Diversity, and Inclusion
Bibliographic record
Abstract
The cybersecurity sector has faced chronic talent shortages in recent years. One potential solution for addressing this issue is to leverage the expertise of immigrants. The literature on immigrants’ integration has predominantly used a human capital lens, which overlooks the structural issues of integration. Whereas equity, diversity, and inclusion (EDI) in organizational literature is more focused on gender and race. There is a need to bridge these approaches in order to create a holistic conceptual framework to guide research into the integration challenges that skilled immigrants face in host countries. This research first identifies the key aspects of immigrant employment outcomes by reviewing literature on immigrant integration and EDI. Using the cybersecurity sector in Canada as a case study, this paper presents a conceptual framework for investigating employment integration of skilled immigrants, which can also be applied to other skilled sectors.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".