Surveying the “Post‐Industrial” Landscape: Information Technologies and Labour Market Polarization in Canada*
Bibliographic record
Abstract
Dans le cours des débats récents concernant l'effet des technologies nouvelles sur le travail, une question touche la polarisation des «bons» et des «mauvais» emplois dans l'économie postindustrielle. Les compétences et les gains figurent au centre des préoccupations. À partir de données tirées de l'Enquête sociale générale de 1994, nous avons examiné l'utilisation de l'informatique au Canada, et nous avons analysé l'incidence de cet usage sur les compétences et les gains liés aux emplois. Nos conclusions n'appuient pas une explication du phénomène de la polarisation fondée sur la technologie dans le marché du travail. Les caractéristiques des travailleurs et les modalités professionnelles sont beaucoup plus importantes, bien qu'il existe des differences rattachées aux competences en infor‐matique dans des regroupements semblables de professions. A key issue in recent debates over the impact of new technologies on work is the polarization of “good” and “bad” jobs within the “post‐industrial” economy. Two dimensions—skillandearnings—have been of central concern. Drawing on the 1994 General Social Survey, we examine computer use in Canada, and analyze its impact on job earnings and skill. Our findings do not support a technology‐based explanation of polarization within the labour market as a whole. Instead, worker characteristics and occupational conditions are far more important, although there is some evidence of computer‐related skill differences within similar groupings of occupations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".