Bibliographic record
Abstract
New organizational arrangements are required to underpin emerging public sector service transformation initiatives with a substantial electronic government (e-government) dimension. These arrangements are both internal to government, involving new collaborative relationships among service delivery agencies and reform of procurement processes, and external, involving the formation and management of strategic relationships between private sector information technology (IT) vendors and public service providers. This article explores the relational context of service transformation by first examining some current initiatives in Canada—at both provincial and federal levels. These case studies reveal the nexus between digital technologies, internal organizational change, and public–private sector interactions. They also reveal the emergence of new collaborative mechanisms between both sectors, especially in the initial phase of relationships where the IT-enabled service transformation is being mutually defined. This heightened level of collaboration also represents a significant departure from traditional government procurement models—where inputs are defined by public authorities and then secured in the marketplace from qualified vendors. E-government—and service transformation initiatives in particular—are consequently driving a rethinking of the role and purpose of procurement mechanisms in an increasingly digital and interdependent environment. Many political and administrative quandaries remain, however, as governments struggle to achieve a balance between traditional public interest principles such as probity, transparency, and accountability, and the rising importance of strategic collaboration. Building on the case studies and a review of current efforts at procurement reform, this article offers and assessment of how this interrelationship between service transformation and public–private collaboration is likely to shape future e-government-based service transformation efforts in Canada.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".