Managing Knowledge and Learning in it Projects: A Conceptual Framework and Guidelines for Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it