Bibliographic record
Abstract
Ross Finnie is with the School of Policy Studies at Queen’s University and the Business and Labour Market Analysis Division at Statistics Canada. He can be reached at 613-533-6000, ext. 74219. Ronald Meng is with the Department of Economics at the University of Windsor. He can be reached at 519-253-4232, ext. 2371. Both authors can be reached at perspectives@statcan.ca. The research paper The Importance of Functional Literacy: Reading and Math Skills and Labour Market Outcomes of High School Drop-outs from which this article is adapted is available on the Statistics Canada Web site at http://www.statcan.ca/english/ research/11F0019MIE/11F0019MIE2006275.pdf. Since the early 1990s, the technology-driven ‘knowledge-based’ economy has captured the attention and affected the lives of virtually all Canadians. This phenomenon has been of particular interest to researchers and policy makers, not to mention business owners, long-time workers, and students permanently entering the job market following graduation or, more troubling, after dropping out of high school. One concern is how those lacking the technical skills, experience and necessary education—beginning with the three Rs—may be left behind in dead-end jobs as their peers pursue more dependable and lucrative career paths.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".