Aligning Canada-Alberta job grant usage with Government of Alberta priorities: Options for program improvements
Bibliographic record
Abstract
Industry and Regional Program UseFirst year program usage suggests that the Construction and Manufacturing sectors are large users of the program.Most industry sectors in Alberta do not make considerable use of the Canada-Alberta Job Grant.However, there is significant small and medium size business program uptake, along with rural areas being well-represented in program participation.Employer applicants tend to use the program to train around three employees for just under two weeks each.The average funding amount per trainee is around $1680 in government contributions, with very few employers seeking overall training contributions above $200,000 up to the program maximum of $300,000.Training is taking place under the Canada-Alberta Job Grant from a variety of training providers.The main training organizations used are postsecondary institutions, private trainers, and industry associations. Trainee and DemographicsAlthough voluntary to disclose, program usage data indicate that very few trainees under the program self-identify as Aboriginal, visible minority, or as a person with a disability.Men are strongly represented, with around 85 per cent of all active application trainees being male.The program is being used to mostly train Canadians in skilled management and non-management occupations, in addition to a contingent of low-skilled management trainees as well.The Canada-Alberta Job Grant is open to new and existing employees.However, the program has been used to almost exclusively train current employees, as opposed to assisting individuals in moving into employment as a result of being trained and hired. Change in Policy FocusThe new Alberta government direction has shifted to emphasize a more social focus.Narratives tend to be around strong communities and families, as well as support for individuals, Aboriginal Peoples, and women.This social focus does not immediately align with current program usage demonstrated through the demand-driven, employer-oriented nature of the Canada-Alberta Job Grant.Current program usage tends to mostly favour individuals who are male and highly skilled in a few particular sectors.Consequently, many groups are not benefiting from the Canada-Alberta Job Grant to the extent as the broader population in Alberta.Consequently, there are opportunities to shape the Canada-Alberta Job Grant to better reflect emerging Government of Alberta priorities and interests.iv
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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 | 0.038 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".