Managing Knowledge and Learning in it Projects: A Conceptual Framework and Guidelines for Practice
Bibliographic record
Abstract
This paper presents a framework identifying the key areas within IT projects where knowledge-based risks occur. These risks include a failure to learn from past projects, competence of the project team, problems in integrating and transferring knowledge, lack of a knowledge map, and volatility in governance. The model was compiled through an extensive literature search encompassing project management, information systems, software development, and team learning literatures. This framework was then tested and modified through a field study of 15 senior project managers from North America and New Zealand. Analysis of the interviews from the field study resulted in a set of five broad principles of knowledge management within projects. These principles relate to a climate for learning, knowledge levels, knowledge channels, team memory, and knowledge risks. Practices suggested by the interviewees accompany each principle.
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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 | 0.066 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.033 | 0.035 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".