Different strokes for differents folks : examining the effects of computerization on Canadian workers
Bibliographic record
Abstract
Computerization (the diffusion of a combination of hardware and software) has accelerated in the last 30 years due to advances in electronic technologies, the advent of the microprocessor and the tremendous development of the software industry. The process of codification has intensified and routine tasks have tended to disappear, changing the architecture of jobs and, therefore, the structure of employment. A number of occupations have become increasingly associated with the computer, and these jobs require highly skilled workers. Using a production function framework, we found that computerization is not labour-saving but is instead labour-using. Despite this general trend, important inter-industrial differences prevail in the association of skills patterns with the computer. By transforming the structure of jobs, the computer has changed the skills requirements: the knowledge, management and data category of workers is closely associated with the use of computers while for good workers the relationship is a substitutive one due to expert systems software. The computer because of the highly tacit nature of the tasks does not affect the service category of workers. Though the uniqueness of the computer revolution should not be exaggerated, the computer has certainly acted as a catalyst given its pervasiveness and its capacity to merge with other technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".