“Make Resilience Matter” for Children Exposed to Intimate Partner Violence Project: Mobilizing Knowledge to Action Using a Research Contributions Framework
Bibliographic record
Abstract
Objective: This article describes using a Research Contribution Framework (RCF) (Morton, 2015a), to plan and document the progress of knowledge mobilization (KMb) efforts for the Make Resilience Matter (MRM) for Children Exposed to Intimate Partner Violence (IPV) study. Research uptake, use and impact activities were planned for this project designed to identify how to foster resilience-informed practice with children exposed to IPV. This KMb strategy is useful for planning and considering how we engage knowledge users, context, environmental impact, unexpected developments, and the complexities of doing research and mobilizing results in the “real world” of practice. The benefits of mapping RCF onto KMb planning and lessons learned may be transferred to other projects. Method: First we outline RCF; second, we describe the MRM project; third we apply RCF to the MRM project detailing a process for engaging knowledge users and planning and tracking research uptake, use and impact. The trans-theoretical theory of change (Prochaska & DiClemente, 1982) is used to understand readiness to change in relation to research uptake and use. An overarching feminist theoretical understanding of gender based violence (Hawkesworth, 2006; Heise, 1998) helps to inform our awareness of the socio-political context. Results: Research uptake, use, and impact as applied to the MRM project are presented. An outcomes chain (Morton, 2015a) is offered to help trace engagement/involvement, activities/outputs, awareness/reactions, knowledge/attitudes, and anticipated practice behaviour change. Four guiding principles emerged from our experience which may helpto inform future KMb efforts.
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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".