Knowledge needs in the non-profit sector: an evidence-based model of organizational practices
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
Purpose – This paper aims to present findings from a study of non-profit organizations (NPOs), including a model of knowledge needs that can be applied by practitioners and scholars to further develop the NPO sector. Design/methodology/approach – A survey was conducted with NPOs operating in Canada and Australia. An analysis of survey responses identified the different types of knowledge essential for each organization. Respondents identified the importance of three pre-determined themes (quantitative data) related to knowledge needs, as well as a fourth option, which was a free text box (qualitative data). The quantitative and qualitative data were analyzed using descriptive statistical analyses and a grounded theory approach, respectively. Findings – Analysis of the quantitative data indicates that NPOs ' needs are comparable in both countries. Analysis of qualitative data identified five major categories and multiple sub-categories representing the types of knowledge needs of NPOs. Major categories are knowledge about management and organizational practices, knowledge about resources, community knowledge, sectoral knowledge and situated knowledge. The paper discusses the results using semantic proximity and presents an emergent, evidence-based knowledge management (KM)-NPO model. Originality/value – The findings contribute to the growing body of literature in the KM domain, and in the understudied research domain related to the knowledge needs and experiences of NPOs. NPOs will find the identified categories and sub-categories useful to undertake KM initiatives within their individual organizations. The study is also unique, as it includes data from two countries, Canada and Australia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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