Finding KM solutions for a volunteer‐based non‐profit organization
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the knowledge needs of a small, volunteer‐based Non‐Profit Organization (NPO) and present recommendations for implementation of KM solutions. Design/methodology/approach The methodology used in this paper is the knowledge audit. Data collection methods include semi‐structured interviews, documentary photography, and a review of content on the NPO's website. Findings The paper recommends a combination of web 2.0 technology and low‐tech solutions to meet the KM needs of the volunteer‐based organization within the constraints of its limited resources. Based on the observation that dedicated and reliable volunteers are critical to this organization's success, the paper proposes that the KM solution address personal knowledge needs related to volunteer motivation factors as a strategy for improving volunteer recruitment and retention. Research limitations/implications The study examined a small group of volunteers engaged in a specialized form of knowledge‐sharing work. Future research could test this paper's conclusions in larger and more diverse volunteer‐based NPOs. Originality/value The paper extends KM research into the realm of volunteer‐based NPOs and adopts elements from Motivation‐Hygiene theory as well as specific volunteer motivation factors as additional criteria for a KM solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".