Bibliographic record
Abstract
This guide was developed to help multi-sectoral community collaborations associated with United Ways – Centraides to assess their “well-being”. Such collaborations address complex issues that require a comprehensive approach. Member organizations and participants develop a common vision, and share leadership and resources to work towards outcomes focused on long-term, systemic changes in the community. The tool is designed to assess the effectiveness of the collaboration’s internal structures \nand processes. It can assist participants in the collaboration to share their perceptions about the effectiveness of current and past processes, and the appropriateness of mechanisms and structures used by the collaboration to accomplish its work. It may be used to facilitate a group discussion or may be adapted for use as a survey or to interview key informants. The tool is intended to help you to develop, sustain and renew your collaborative initiative. However, it is not designed to help you assess the effectiveness of specific strategies that your collaboration may implement, such as public education or public policy advocacy, etc. While this tool refers to collaborations that work to improve outcomes for children and families, it can be easily adapted to a different focus. The tool was developed based on: a review of tools developed by other organizations; a review of literature on success factors in collaborations, and improving the sustainability of community collaborations; the UW-C Movement’s Standards of Excellence; and \n the experiences of children’s initiatives associated with United Ways – Centraides in Canada.
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 | 0.031 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.091 | 0.034 |
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".