Standardising public service: the experiences of call-centre workers in the Canadian federal government
Bibliographic record
Abstract
This paper explores the impact of the adoption of neoliberal economic policies and practices on public sector jobs within the Canadian Federal government. In recent years, employment in the public sector has been increasingly shifted to a call-centre format, thereby transforming the working conditions of public servants as well as access to services enjoyed by Canadians. By adopting work practices, technologies and managerial techniques usually found within the private sector, we argue that the call-centre format fundamentally transforms the notion of public ‘service’ from secure employment and a dynamic career to that of a routine, Taylorised job. In this process, standardised interactions redefine the notion of public service and the role of the public servant.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.078 | 0.028 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".