Applying the Experience Effectiveness (XE) Framework in the Canadian Public Sector
Bibliographic record
Abstract
Why is it so hard to interact with government services? The public sector has become citizen centered in designing and collaborating with the community to improve service. Even though governments invest in efforts to ensure public administration is aligned with the needs of the community, services still fail to meet the standards provided by equivalent private‐sector organizations. Citizen experiences fall short of expectations due to inadequate performance evaluation for the delivery of integrated and well‐designed services. Public‐sector performance measures must assess and include the impact that services have on citizens. This article describes the extension and further development of the Experience Effectiveness ( XE ) Measurement Framework. If properly utilized, public‐sector organizations can implement the framework to evaluate the effectiveness of citizen experiences based on human‐centered, universal, and systems‐thinking heuristics. Through a multiphase mixed‐method design, we test the XE Framework and its operational development with two projects in the Innovation Lab for the Canadian Department of Innovation, Science and Economic Development. The case studies demonstrate that the XE Framework clearly differentiates the quality of the experience and identifies areas for improvement. Results also indicate that the bureaucracy distorted the creation and delivery of the service citizens received. Organizational culture, climate, structures, and values significantly shape the outcome and provision of government services, which raises further questions about design and innovation in public administration and the role of accountability.
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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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| 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".