Towards a unified knowledge management framework in non-profit sector: The case of Canada
Bibliographic record
Abstract
The interest in knowledge management (KM) and its capabilities exists in all business sectors. Since 2009, and building on the result of Heisig's (2009) study, a few researchers have tried to create a new unified knowledge management framework, such as Evans et al. (2015) and Shongwe (2016). These frameworks and other heterogeneous knowledge management frameworks have been presented as holistic solutions that meet all sectors’ needs globally. Aiming to assist non-profit organizations implementing knowledge management programs, and as a part of a PhD thesis, this qualitative case study provides a practical and effective holistic KM framework dedicated to guiding NPOs in achieving their goals in serving surrounding communities and countries. This paper highlights pertinent issues in Knowledge Management framework development and implementation, which enhances the academic understanding and the practical implementation avenues for KM researchers and managers in the non-profit sector by suggesting common components of KM programs in NPOs led by a framework. This study is unique in presenting knowledge management components and framework derived from NPOs’ country, language, and culture to meet their specific needs and guide them in implementing successful KM programs.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".