Bibliographic record
Abstract
Purpose The purpose of this paper is to provide details on how the Capital Projects Division within Alberta Infrastructure, in the provincial government of Alberta, started its journey to excellence by following Excellence Canada's framework. The framework focusses on systematic approach to excellence and widespread implementation in the organization. This paper provides comprehensive information on the division's processes, trends and impending changes for leadership that demonstrates quality and commitment of business excellence through quality improvements based on experience. Design/methodology/approach In partnership with Excellence Canada, Capital Projects Division of Alberta government embarked on a journey to excellence using the Progressive Excellence Program® framework for quality. Equally important, the division explored ways it can invest wisely in innovative ideas that will reshape the current organization and prepare staff for a very exciting future. That meant using the most comprehensive approach to review existing processes and strive for efficient and innovative ideas for continuous improvement over the longer term. Findings First, incorporating quality in the workplace is about the journey, not the destination, so leadership plays a vital role in its success. Second, the strategies on how to achieve quality primarily originate from the people within the organization. A key to achieving quality is to provide a framework for these ideas and strategies to emerge. Originality/value This paper focuses on the perseverance and leadership required in the development of a framework to support and encourage quality in the organization.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".