Bibliographic record
Abstract
Foreword by Israel Gat The Agile Software Development SeriesCockburn HighsmithSeries Editors Creating Innovative Products Software Development/Agile Best practices for managing projects in agile environmentsnow updated with new techniques for larger projects Today, the pace of project management moves faster. Project management needs to become more flexible and far more responsive to customers. Using Agile Project Management (APM), project managers can achieve all these goals without compromising value, quality, or business discipline. In Agile Project Management, Second Edition, renowned agile pioneer Jim Highsmith thoroughly updates his classic guide to APM, extending and refining it to support even the largest projects and organizations. Writing for project leaders, managers, and executives at all levels, Highsmith integrates the best project management, product management, and software development practices into an overall framework designed to support unprecedented speed and mobility. The many topics added in this new edition include incorporating agile values, scaling agile projects, release planning, portfolio governance, and enhancing organizational agility. Project and business leaders will especially appreciate Highsmiths new coverage of promoting agility through performance measurements based on value, quality, and constraints. This editions coverage includes: Understanding the agile revolutions impact on product development Recognizing when agile methods will work in project management, and when they wont Setting realistic business objectives for Agile Project Management Promoting agile values and principles across the organization Utilizing a proven Agile Enterprise Framework that encompasses governance, project and iteration management, and technical practices Optimizing all five stages of the agile project: Envision, Speculate, Explore, Adapt, and Close Organizational and product-related processes for scaling agile to the largest projects and teams Agile project governance solutions for executives and management The Agile Triangle: measuring performance in ways that encourage agility instead of discouraging it The changing role of the agile project leader Jim Highsmith is a founding member of the AgileAlliance, co-author of the Agile Manifesto, and director of the Agile Project Management Advisory Service for the Cutter Consortium. He consults with development organizations throughout the U.S., Europe, Canada, South Africa, Australia, Japan, India, and New Zealand on accelerating development in todays increasingly complex, uncertain environments. Highsmith is author of Adaptive Software Development, winner of the 2000 Jolt Award, and (with Alistair Cockburn) co-editor of The Agile Software Development Series. He has more than 25 years experience as an IT manager, product manager, project manager, consultant, and software developer.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.044 |
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".