Technological change, learning, and capitalist globalization: outsourcing step-by-step in the Canadian public sector
Bibliographic record
Abstract
Drawing on original interviews (n=70) and survey research (n=336) on technological change in Canadian public sector welfare work, this article explores the role of work design and change, with an emphasis on the political economic dimensions of centralized and localized knowledge systems, cooperation, and worker resistance. While it is necessary first to establish how it is that work and technological design are interwoven with learning responses, the article concludes with a discussion of implications concerning public sector work transformation and, ultimately, the capacity for the state to outsource public services. Résumé Dessinant sur l’entrevue originale (n=70) et la recherche d’aperçu (n=336) sur le changement technologique du travail canadien d’assistance sociale de secteur public, cet article explore le rôle de la stylique et le changement avec une emphase sur les dimensions économiques politiques des systèmes de la connaissance, de la coopération et de la résistance centralisés et localisés d’ouvrier. Tandis qu’il est nécessaire de d’abord établissez comment c’est que le travail et la conception technologique sont entrelacés avec des réponses d’étude, l’article conclut avec une discussion des implications au sujet de la transformation de travail de secteur public et, finalement, de la capacité pour que l’état externalise des services publics.
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 | 0.003 | 0.007 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".