The Barriers that Low-skilled Workers Might Face to Canada Lifelong Learning Plan Participation
Bibliographic record
Abstract
The rapid development of society under today's Canadian neoliberal control. From a cultural to an economic standpoint, human labor has become a critical component in the operation of this nation. However, Canada is facing a serious aging population problem that will make it impossible to meet society's labor resource needs. The federal government issued a policy called the Lifelong Learning Plan (LLP) in 1999 to support citizens' lifelong learning and training, which may extend their working period after they retire. Nonetheless, given the lower wealth accumulation and working skills of low-skilled employees, there may be some barriers to access LLP after retirement and returning to the workplace. This paper investigates the barriers that low-skilled workers may face in accessing LLP, finishing their post-secondary education, and returning to the workplace in Canada using a qualitative method of analyzing official government policies and academic literacy. The paper discovers that there are several limitations that low-skilled employees may face in order to participate successfully and optimally, including: low wealth accumulation to support the high tuition fee; social conflict social roles of being full-time students in LLP and full-time employees from workplace; regular long training period from LLP; and other chosen investment options that are more important than LLP from Registered Retirement Saving Plans (RRSPs)
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".