Conceptualizing the elements of research impact: towards semantic standards
Bibliographic record
Abstract
Abstract Any effort to understand, evaluate, and improve the impact of research must begin with clear concepts and definitions. Currently, key terms to describe research results are used ambiguously, and the most common definitions for these terms are fundamentally flawed. This hinders research design, evaluation, learning, and accountability. Specifically, the terms outcome and impact are often defined and distinguished from one another using relative characteristics, such as the degree, directness, scale, or duration of change. It is proposed instead to define these terms by the kind of change rather than by the degree or temporal nature of change. Research contributions to a change process are modeled as a series of causally inter-related steps in a results chain or results web with three main kinds of results: (i) the direct products of research, referred to as outputs; (ii) changes in the agency and actions of system actors when they are informed/influenced by research outputs, referred to as outcomes; and (iii) tangible changes in the social, economic, environmental, or other physical condition, referred to as realized benefits. Complete definitions for these terms are provided, along with examples. This classification aims to help focus research evaluation appropriately and enhance appreciation of the multiple pathways and mechanisms by which scholarship contributes to change.
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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 | 0.144 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.023 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.027 | 0.053 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".