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Record W2945434485 · doi:10.56645/jmde.v15i32.513

Communication for Social Change

2019· article· en· W2945434485 on OpenAlexaffabout
Ricardo Ramı́rez, Wendy Quarry

Bibliographic record

VenueJournal of MultiDisciplinary Evaluation · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDeci-Capacity buildingPublic relationsPolitical scienceKnowledge managementBusinessComputer science

Abstract

fetched live from OpenAlex

Background: Communication for social change is rarely a stand-alone initiative. More often it is combined with several communication purposes such as networking, organizational visibility, information dissemination, or behavioural change. Purpose: This article reports on an inter-disciplinary, capacity building experiment that combines communication strategy development with Utilization-Focused Evaluation (UFE). Setting: The analysis stems from close to a dozen case studies where we tested a hybrid approach of UFE and communication strategy development. Our partners were research teams in a variety of areas including open education, open and collaborative science, Internet privacy, cyber-security, and open data. The Networked Economies Program of the International Development Research Centre (IDRC, Ottawa) funded each one of the research teams. The partners were based in different countries and had a global reach. Intervention: The authors are members of a research project entitled “Designing Evaluation and Communication for Impact” (DECI) that provides mentoring in evaluation and communication to partners. This article focuses mainly on lessons from DECI-2, the second phase of the project that was operational from 2012-2017. DECI is led by a team in Canada and has engaged regional mentors based in Latin America, Asia and East Africa, who have provided much of the capacity building support to partners in their regions. At the end of each mentoring cycle, the DECI team produced a case study summarizing the experience. The collection of these case studies is the basis for this article. Research Design: This article is a meta-evaluation of the experiences gained from the mentoring. It brings the findings from the grounded work and seeks to find theoretical insights from the evaluation and communication literature. Existing family trees in evaluation and communication are reviewed in search for commonalities that underlie the hybrid decision-making framework. Data Collection and Analysis: The article leans on the findings of the case studies and the hybrid framework. Our analysis builds on earlier work by the authors in communication for social change. In particular, we analyze a common pattern where communication strategies tend to encompass several purposes in tandem. We refer to the planning steps in utilization-focused evaluation as a structured decision-making process that can help organize communication planning. Finally, we reflect on the benefits of formulating communication objectives that can be tracked or measured. Findings: The hybrid decision-making framework allows communication planners to add some rigor to their strategies. At the same time, it invites evaluators to introduce evaluation questions about the outcomes of a communication intervention. An external evaluation of the DECI-2 project concluded that the combined decision-making process enabled partners to become better at adaptive management. The process introduced reflection spaces and helped teams adjust their projects as research findings emerged, and as conditions shifted in the policy arenas that they sought to influence.

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 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)0.026
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.021
Scholarly communication0.0150.013
Open science0.0020.015
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0540.011

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 head0.492
GPT teacher head0.583
Teacher spread0.090 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2019
Admission routes2
Has abstractyes

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