Use of Technology in Non-Profit Organizations (NPOs) for Knowledge Management
Bibliographic record
Abstract
This paper explores results of a survey that documented tools and technologies used to manage knowledge in Canadian non-profit organizations (NPOs). Findings demonstrate that NPOs, across various types of organizations, use both non-computer (e.g., print documents) and computer-based solutions to manage knowledge. Examples of tools/technologies used include donor management software, email-based systems for communication and marketing, and some specific tools relevant to their areas of operations. Cet article explore les résultats d'une enquête qui a documenté les outils et les technologies utilisés pour gérer les connaissances dans les organisations canadiennes sans but lucratif (OSBL). Les résultats démontrent que les différents types d’OSBL utilisent à la fois des solutions informatiques et non-informatiques (par exemple, des documents imprimés) pour gérer les connaissances. Les outils / technologies utilisées comprennent des logiciels de gestion des donateurs, des systèmes basés sur le courrier électronique pour la communication et le marketing, et quelques outils spécifiques pertinents dans leurs domaines d'activité.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".