Neo-liberalism: Employability, and the Labor Market Mobility among Racialized Migrants- Canada's Study
Bibliographic record
Abstract
The paper discusses the effect of political paradigm on the path of career development and the predicament of employment outcomes among racialized migrants in Canada. The study highlights challenge of retraining, skills development and access to Canadian work experience that meets neo-labor market demands. The study also examines how neoliberals’ interventions in market place, elimination of social services, and employment support programs have deterred labor force integration of the racialized migrants’ job seekers. In this Grounded Theory study (GT), participants have shared their experiences and challenges they have encountered form own perspectives. They shared stories about difficulties of finding suitable training and employment support programs within the current neo-liberalized labor market in Canada. The outcomes suggested that the rise of neoliberalism as noted in policies of social and employment services cuts, coupled with employment standard Acts reforms (ESA), have given employers more powers over hiring process which in many cases has nothing to do with candidate’s skills or qualifications. In this neo-political paradigm, the racialized migrants felt they have wasted most of their productive years searching for (1) training, mentorship or employment support programs that can facilitate effective transition to the labor force, and (2) dealing with challenges of improving unrecognized skills and qualification attained from countries of origin.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.006 |
| 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".