MétaCan
Menu
← Back to cohort

A Network-based Approach to Brokering Research Evidence for Impact

2021· report· en· W4255735068 on OpenAlexfundno aff
Clark Louise, Grace Lyn Higdon

Bibliographic record

Venuenot available
Typereport
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
FundersMedical Research CouncilArts and Humanities Research CouncilEconomic and Social Research CouncilForeign, Commonwealth and Development OfficeDepartment for International DevelopmentUniversity of BathGlobal Challenges Research FundUniversity of LiverpoolUniversity of CambridgeEngineering and Physical Sciences Research CouncilUK Research and InnovationOverseas Development InstituteInternational Development Research CentreGovernment of the United KingdomOxfam AmericaUniversity of NottinghamImperial College LondonLeeds Beckett University
KeywordsComputer scienceData science

Abstract

fetched live from OpenAlex

is the Communications and Impact Manager at IDS.She leads the communications for a portfolio of international development research projects and manages a team of research communications specialists.She has worked on a range of issues within the sector including health and nutrition, child labour, and gender equality with a focus on translating evidence and knowledge into accessible and engaging communications to achieve sustainable impact.As part of the Impact Initiative communications team, she has helped shape the impact storytelling process, led high-level policy events, and developed cross-synthesis policy papers.Louise Clark is the Monitoring Evaluation and Learning (MEL) Manager at IDS and has been mapping social networks for over 15 years to visualise how different groups interact and collaborate and how these relational structures facilitate knowledge exchange and support positive development outcomes.Her work involves all stages in the MEL cycle, from the strategic design of MEL frameworks and approaches, creating MEL processes and products, supporting monitoring and reporting to managing evaluations and facilitating spaces for reflection and learning.She has a particular interest in Theory of Change as a tool to support stakeholder engagement and build shared ownership of project outcomes to deliver strategies that promote behaviour change.Her facilitation supports projects to explore causal pathways and challenge assumptions about how change happens to promote reflection, learning, and improvement.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Evaluation · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.211
metaresearch head score (Gemma)0.256
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2110.256
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0350.023
Science and technology studies0.0120.018
Scholarly communication0.0400.046
Open science0.0090.032
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0330.004

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 head0.917
GPT teacher head0.728
Teacher spread0.189 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
DomainEvaluation
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicEvaluation and Performance Assessment→CategoryMetaresearch→French-language works237,207→