Learning from Project Experience: Creating, Capturing and Sharing Knowledge
Bibliographic record
Abstract
Project-based organizations encourage transfer of lessons learned within and across projects to avoid duplication and repetition of mistakes, save time and improve efficiency. Drawing on cognitive and situated learning theories, we conducted a qualitative inquiry into the process of learning from past project experiences in an international project- based organization located in Uganda. We studied how project team members at three regional offices of the organization create, capture and share project knowledge with a view towards organization-wide learning. After analyzing data from a web-based survey and personal interviews, we found that project knowledge is highly tacit and for the most part embedded in practice. Knowledge about the project was encoded in project reports and shared with clients as part of standard project process, however, knowledge of how the project was executed was informally shared within the project team and only with others on a need to know basis. Learning across project boundaries is difficult to articulate, therefore, project based organizations stand to benefit from integrating social interactions in their formal, structured learning systems.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".