Disseminating, Assimilating, and Creating: A Social Knowledge Cycle Model for Non-Profit Organizations
Bibliographic record
Abstract
This research paper explores how ‘social knowledge’, as an emergent category of organizational knowledge, flows through non-profit organizations (NPOs). Examining findings from qualitative interviews with 16 individual from Canadian NPOs on their use of social media for Knowledge Management (KM), the paper builds on KM theories and epistemologies to propose a model for the assimilation, dissemination and creation of ‘social knowledge’ in NPOs.Ce rapport de recherche examine comment la «connaissance sociale», en tant que catégorie émergente de la connaissance organisationnelle, circule dans les organisations sans but lucratif (OSBL). Nous avons examiné les résultats d’entretiens qualitatifs avec seize personnes provenant d'OSBL canadiennes sur leur utilisation des médias sociaux aux fins de la gestion des connaissances. Notre rapport s'appuie sur les théories et l’épistémologie de la gestion des connaissances pour proposer un modèle de l'assimilation, de la diffusion et de la création de «connaissance sociale» dans les OSBL.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".