Understanding the types of knowledge representations that meet non‐profit organizations’ knowledge needs
Bibliographic record
Abstract
ABSTRACT The not‐for‐profit sector is an important area for research, since the organizations operating in this domain contribute in many ways to our society (e.g., social value and GDP). Non‐profit organizations (NPOs) are highly knowledge‐oriented units. Knowledge management (KM) has been researched in breadth and depth in FPOs, where it has been demonstrated that KM plays a significant role in the success of these organizations. According to the literature, KM is equally important to non‐profits, and yet the sector has received comparatively scant attention in the KM literature. There is limited understanding on the knowledge needs of NPOs and thus, this paper seeks to provide insight into the NPO‐KM landscape. The authors conducted multi‐phase research with NPOs operating in different parts of Canada, including an online survey of Canadian charities. Through the analysis of survey results, twelve broad categories of knowledge types and their sub‐categories relevant to NPOs have been identified. This paper aims to contribute generally to the growing body of KM literature (i.e., beyond dichotomous model of tacit and explicit knowledge) and more specifically to the NPO‐KM space.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".