Sensory Methodologies and Methods: A Scoping Review
Bibliographic record
Abstract
This scoping review examines the application of sensory research methodologies and methods in primary research, guided by Arksey and O’Malley’s five-stage framework. The scoping review addresses two primary questions: (1) what is the extent and nature of research activities that use multisensory methodologies and (2) what is the extent and nature of research activities that use multisensory methods? The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist was used to guide the reporting and mapping process. A total of 80 sources (45 peer-reviewed articles and 35 dissertations) met the inclusion criteria. Findings reveal ethnographic-based methodologies were the most common sensory approach, whereas combined visual and audio methods were the most commonly used techniques. There is the potential for more innovative and inclusive methodologies and methods to expand the use of taste and smell, which remain underrepresented in the literature. Additionally, greater attention is needed to address power dynamics and reflexivity in sensory research to avoid essentializing or misrepresenting participants’ experiences. Future research could improve methodological clarity and consistency while emphasizing accessibility and community engagement. This scoping review contributes to the field of sensory research by synthesizing current practices and identifying gaps that warrant future exploration, particularly in underrepresented sensory modalities.
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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 | 0.140 | 0.269 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.045 | 0.034 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".