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Record W2569751145

Collaboration Assessment Guide and Tool

2007· article· en· W2569751145 on OpenAlexaboutno aff
M Kellerman

Bibliographic record

VenueThe Atrium (University of Guelph) · 2007
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.370
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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