Transforming service to Canadians: the Service Canada model
Bibliographic record
Abstract
Over the past decade, the pursuit of citizen-centred service, combined with rapid advances in information and communication technologies, has stimulated innovative approaches to the organizational design of governments' service delivery systems. Service delivery organizations in Canada and elsewhere have taken a variety of organizational forms, thereby providing a range of models for adoption or adaptation. Service Canada offers Canadians a new model for the delivery of government services. It is a one-stop, multi-channel and multi-jurisdictional initiative that is dedicated to delivering seamless citizen-centred service. It brings together a wide range of government programmes and services from across federal departments and other levels of government to provide citizens with integrated, easy-to-access, personalized service. This article assesses the possibilities that the Service Canada model presents for service transformation through integrated service delivery (ISD) and discusses political, structural, operational, managerial and cultural barriers to its implementation. Points for practitioners Successful ISD initiatives can take a variety of organizational forms with an array of governance arrangements. While some of the ISD challenges are not faced by all countries, many of the challenges (e.g. privacy and security issues) are of a generic nature. Many of the solutions to ISD challenges are also of general application, including those utilized by Service Canada — the innovative use of partnerships, adequate funding, guaranteed privacy and security and effective human resource management. Note also that successful service transformation requires the creation of a culture of service excellence among employees, the demonstration of frequent and tangible results, and understanding that leadership in service integration requires a capacity for adapting to an ambiguous and ever-changing environment.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".