Non-Profits and the Use of Social Technologies for Knowledge Management
Bibliographic record
Abstract
This poster presents key outcomes and emerging findings from ongoing research examining the role of social technologies such as blogs, wikis, and social networks (e.g., Twitter, Facebook, LinkedIn) for creating, sharing, gathering and managing knowledge in non-profit organizations (NPOs). Mixed methods research including qualitative interviews with 16 Canadian NPOs and a national online survey of NPOs have generated a number of key findings on the role of social media as KM tools in the not-for-profit sector. These findings help us understand the implications of global connectedness, as manifested in social media, on the KM practices of these organizations.Cette affiche présente les principaux résultats et conclusions issues d’une recherche en cours qui examine le rôle des technologies sociales comme les blogues, les wikis et les réseaux sociaux (par ex., Twitter, Facebook, LinkedIn) pour la création, le partage, la collecte et la gestion des connaissances dans les organisations à but non lucratif (OSBL). La recherche, effectuée en utilisant un mélange de méthodes, y compris des entretiens qualitatifs avec seize OSBL canadiennes et un sondage national en ligne, a généré un certain nombre de conclusions-clés sur le rôle des médias sociaux comme outils de gestion des connaissances dans le secteur des organisations sans-but-lucratif. Ces conclusions nous permettent de comprendre les implications de l’interconnexion au niveau mondial, telle qu'elle se manifeste dans les médias sociaux, par les pratiques de gestion des connaissances dans ces organisations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".