Public sector enterprise resource planning : issues in change management
Bibliographic record
Abstract
Contents: Preface PART I: PUBLIC SECTOR VIEW 1. Battlespace ERP Systems: Changing the Way Military Decision Makers Think in a Net-Centric Information Environment Grayson Morgan 2. Enterprise Resource Planning: Commerce Administrative Management System Lisa K. Westerback 3. Change Management in the Canadian Forces: Land Force Atlantic Area Headquarters LCol. Robert Russell 4. Emerging Doctrines of Government Performance and Federal ERP Change Management Daniel L. Cuda PART II: CONSULTING VIEW 5. Challenges of Implementing Enterprise Resource Planning (ERP) Applications within the Department of Defense (DoD) Micheline Lopez-Estrada 6. Business Process Change Management: For ERP and Other Public Sector Projects Mathias Kirchmer 7. Establishing Process Ownership by Aligning SAP and DoD Business Processes David Bailey, Thomas Gulledge and Georg Simon 8. Public Sector Change Management: What are the ERP Issues? Rainer A. Sommer PART III: RESEARCH VIEW 9. A Recurring Improvisational Change Management Methodology in Public Sector Enterprise Resource Planning Implementation Cheryl A. Darlington 10. Transaction Costs in Public Sector Information Technology Implementations Douglas Frye 11. Testing Cultural Barriers to Enterprise System Implementations: Change Management through Organizational Culture Assessment Mary A. Leary 12. The Art of Public Sector Information Systems Management: The Role of the Task in public Sector ERP Implementations Carsten Svennson Index
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".