Collaborative networks and non-profit art organizations : the case of Art City
Bibliographic record
Abstract
This major degree project studies the role of networks and their effect on determining the strength and sustainability of Non-profit Art Organizations (NPAO's).This practicum shows how Art City in Winnipeg demonstrates the extent to which partnering organizations share knowledge and experience, and support and influence the strength of NPAO's.My research explores the strength and sustainability NPAO's derive from their embedded networks.These networks can be very complex and may involve many intangible linkages.Non-profit networks have been defined as relational links through which people can obtain access to material resources, knowledge and power (Hillier 2000: 35).This study was the first stage in identifying that relationships existed, and were important to the operation of the organization.Leadership plays an important role in the creation and development of these networks.NPAO's need leaders, or champions, to enrich their organization and to align them with other organizations in a collaborative manner.The climate for NPAO's has changed within the last decade, posing new threats and opportunities.lncreasingly, there is a need to strengthen the organizing, planning, and development capacity of NPAO's (Reardon 1998).Relationships among non-profit organizations and other organizations are becoming increasingly complex and strategic (Drucker 2000: 14).These relationships are transforming from charitable relationships between benevolent donors and grateful recipients, to varied networks that qeate diverse benefits for the organizations involved.Today there is growing interest in the varied range of collaborations between non-profit organizations and businesses.Additionally, there is an increased awareness of the benefits of NPAO's among other sectors and communities (Arts Network for Children and Youth 2002).My case study research on Art City is significant for directors of NPAO's who are interested in better understanding their relationship with other NPAO's, the potential strengths achieved by forming networks and gaining a deepened understanding of strategic planning.This study is also useful for practitioners who function with or within the networks of NPAO's to gain insight into the importance of their role in the network and the opporlunities they have in working with other NPAO's..............
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".