Social media and community knowledge: An ideal partnership for non‐profit organizations
Bibliographic record
Abstract
Abstract Non‐profit organizations (NPOs) must manage knowledge to be relevant, sustainable and competitive. The published literature suggests that stories can be effective for sharing knowledge and making tacit knowledge explicit; however, researchers have not examined storytelling as a knowledge management practice in NPOs in any depth. Similarly, few studies explore the roles of social media in NPOs, including their usefulness for knowledge management practices. This paper reports the results of a research study that examined how NPOs are using social media, with a particular focus on knowledge management practices. Qualitative interviews with 16 staff members working in a range of NPO environments (such as health, library and social services organizations) were conducted. The findings point to the value of storytelling for sharing the organization's mission, for monitoring the NPOs reach into the community, and as a mechanism for gathering knowledge from clients and other key stakeholders.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".