Bibliographic record
Abstract
Purpose The purpose of this review is to offer a summary of visual and projective research methods that have been applied or may be applied fruitfully in an Asian context. Examples are provided and a delineation of the strengths and weaknesses of the methods is made. Design/methodology/approach This is a review article covering a number of different relevant methods and briefly reviewing studies that have been conducted in Asia using these methods. Findings The paper reviews five different uses of qualitative visual and projective methods in Asian consumer and market research: as archival data for analysis; as direct stimuli for data collection; as projective stimuli for data collection; as a means for recording qualitative data; and as a means for presenting qualitative findings. Research limitations/implications It is suggested that Asia contains a rich visual culture and that the research techniques reviewed offer compelling means for enhancing data collection, data analysis, and findings presentations from qualitative market and consumer research in Asia. Originality/value The paper brings together a diverse array of prior research illustrating the potential of the methods reviewed. In addition to discussing this research a number of references are provided for those wishing to examine these methods in greater detail and apply them to their own research.
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 | 0.071 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".