Bibliographic record
Abstract
Charities, also called voluntary-service not-forprofit organizations (VSNFP), play a vital role in modern societies by addressing needs and providing services that benefit the public. These services frequently are available from neither markets nor governments. Many charitable organizations have been created to deliver or have expanded their range or scope of services as the result of governments “devolving” or transferring services to the non-profit sector (Gunn, 2004). Therefore, it is unsurprising that charities have a significant impact economically and socially. For example, volunteer work in Argentina, the United Kingdom, Japan, the United States, and is valued at 2.7, 21, 23, and 109 billion (US) dollars respectively (Johns Hopkins University, 2005). Volunteering translates into significant resources for non-profit organizations. For example, Statistics Canada estimates that work equivalent to 1 million fulltime jobs was provided through volunteer labor in 2004 (Statistics Canada, 2006). While charities are part of the non-profit sector, research demonstrates that charitable organizations differ from for-profit organizations in terms of their human capital management, management practices, and strategies (Bontis & Serenko, 2008). Failing to account for such differences may adversely affect theory (Orlikowski & Barley, 2001) and practice (Kilbourne & Marshall, 2005). Our key question is: What is the extent of our understanding of the role of knowledge management, both as process and system, in charitable organizations? We discuss this question by adapting the knowledge management (KM) research framework originally developed for examining KM in knowledge-based enterprises (Staples, Greenaway & McKeen 2001). Many non-profits are “knowledge-intensive” organizations (Lettieri et al 2004:17). Therefore, this research model should be transferable to non-profit organizations including charities.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".