Worker responses to technological change in the Canadian public sector: issues of learning and labour process
Bibliographic record
Abstract
This article reports selected findings from a study on the changing nature of work, learning and technology in the Canadian public sector (Ontario). Vis‐à‐vis the involvement of a major management consultant firm, these findings mirror the experiences at the nexus of policy, labour process and technology, seen in several other western countries. The authors examined workers’ learning responses to management‐led introduction of a leading edge, Web‐based social service delivery system. The paper shows how neo‐Taylorist principles have shaped work design, and argues that the result has been a high‐tech form of “de‐skilling” (Braverman) in which semi‐professionalized case management workers’ skill/knowledge sets have been systematically broken down. The process has been contested however. Workers have sought to learn and re‐skill, generating not only specific computer‐based skills (or “work‐arounds”) but more general, collective cultures of learning within the everyday life of work. This learning is sometimes in keeping with managerial interests, and sometimes not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".