Peer Learning Event on Sustainability of Grantmaker Associations and Support Organisations
Bibliographic record
Abstract
A report on peer learning event held June 29 - July 1, 2006 in Istanbul, Turkey. Sustainability is a key issue for most grantmaker associations and the group that met for the peer learning event (PLE) on this topic from 29 June to 1 July 2006 was a good cross-section of the field. Some of the participants were from organisations two years old or less, while two others were among the tiny handful created in the 1940s. Size and range of members varied widely as did levels of experience of the participants themselves. Some have had concentrated periods to assess and stabilise their organisations while others are developing sustainability strategies. Two organisations present, while sharing the role of promoting and supporting philanthropy, are not based on a membership structure and thus brought yet another perspective. The countries involved were Brazil, Canada, Ireland, Kenya, Malaysia, the Philippines, Portugal, Romania, South Africa, Turkey and the USA (one organisation US-wide, others covering Northern California, South Florida, and south western states): thus the cultural, economic, legal and historical contexts were extraordinarily different too. The differences did not dominate discussion but in some ways enhanced it with multiple perspectives and varied approaches, and occasionally requiring some careful teasing out of the meaning of terms or tactics in different contexts -- always an enlightening process.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".