UPSKILL: A Credible Test of Workplace Literacy and Essential Skills Training
Bibliographic record
Abstract
The last decade has seen an increasing volume of research that reveals significant gaps in literacy and Essential Skills (LES) among the Canadian workforce. In addition to having negative impacts on firms’ productivity, research also suggests that workers suffer consequences of low literacy in the form of lower wages, reduced job stability, and even higher health risks from workplace injury. While much anecdotal evidence suggests that LES training may be helpful in eliminating these skills gaps, its use in the workplace remains low relative to the size of the apparent need. Part of the reason for low firm investments is the lack of a credible business case providing clear evidence of its return on investment. The Office of Literacy and Essential Skills (OLES), a branch of Employment and Social Development Canada, aimed to fill this gap by evaluating workplace LES training with the most rigorous evaluation methods. In partnership with the Social Research and Demonstration Corporation (SRDC), the UPSKILL project was launched in 2010. A Pan-Canadian research and demonstration project, UPSKILL utilized a random assignment design to provide the most reliable measures of the impacts of LES training in the workplace. Over 100 firms and nearly 1,500 workers in the Accommodations sector were enrolled across the country in eight provinces. The findings indicate that workplace LES training does, indeed, have large positive impacts on workers’ skills, job performance, and a range of economic and social outcomes for workers and firms. A benefit-cost analysis also reveals a fairly significant positive return on investment. Importantly, the study also finds that the pattern of impacts vary significantly across firms in ways that have important implications for the design and delivery of effective training programs. Understanding these factors can lead to policies that support both larger employer investments in workplace training and higher return on investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".