Unravelling Indian migrant workers’ perceptions about the effectiveness of employment assistance programmes in Auckland, New Zealand
Bibliographic record
Abstract
Migrant settlement and integration into the New Zealand labour market has been a longstanding concern for government agencies, migrant community groups, and researchers. Numerous initiatives to enable successful settlement have been created but the effectiveness of these initiatives is questionable as migrants continue to face challenges in finding suitable employment. Non-standard work and under-employment are common experiences among new migrants in New Zealand. These experiences are not unique to New Zealand. Studies show that migrants in countries such as Canada and Australia share similar issues. There is also consensus in the literature for the need to consider the migrants’ points of view regarding the effectiveness of settlement initiatives. As few New Zealand studies have taken the migrant perspective into account, this study fills a research gap by looking at the effectiveness of various employment assistance programmes through the eyes of its migrant participants. \n \nThe first aim of this study, therefore, was to understand what role an employment assistance programme (EAP) plays in facilitating migrants’ settlement in employment in the host country. The second aim of the study was to determine how effective EAPs are in Auckland, New Zealand. The third aim was to critique the Auckland-based EAPs by identifying their strengths and weaknesses. These aims were addressed through the migrants’ perspectives. In addressing these research aims, a qualitative descriptive interpretive approach was applied to capture the different points of views of the interviewees. \n \nThis study found that the EAPs in Auckland are only partially successful in supporting the migrants’ settlement in employment. Despite attending the EAPs, migrants do not find the right employment – employment that is in line with their educational qualifications and prior work experience. Although the EAPs provide useful labour market information, their effect on reduction of barriers is negligible. \n \nThrough its findings, this study provides insights for improvement of the current EAPs and offers a starting point for future evaluative migrant-centred research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".