Assessing the Societal Impact of Research: The Relational Engagement Approach
Bibliographic record
Abstract
Marketing and policy researchers aiming to increase the societal impact of their scholarship should engage directly with relevant stakeholders. For maximum societal effect, this engagement needs to occur both within the research process and throughout the complex process of knowledge transfer. The authors propose that a relational engagement approach to research impact complements and builds on traditional approaches. Traditional approaches to impact employ bibliometric measures and focus on the creation and use of journal articles by scholarly audiences, an important but incomplete part of the academic process. The authors recommend expanding the strategies and measures of impact to include process assessments for specific stakeholders across the entire course of impact, from the creation, awareness, and use of knowledge to societal impact. This relational engagement approach involves the cocreation of research with audiences beyond academia. The authors hope to begin a dialogue on the strategies researchers can use to increase the potential societal benefits of their research.
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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 | 0.091 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".