Bibliographic record
Abstract
Abstract This paper makes the case that there is considerable untapped potential for qualitative research to make theoretical contributions that will advance our collective insights on consumer psychology. The paper explains some features that distinguish qualitative research from other approaches and addresses some common misperceptions about it. It explains why qualitative research—which is geared toward theory development and refinement—can be such as useful took in the kit of researchers seeking insights on consumer psychology. It then outlines a qualitative research process suitable for crafting conceptual contributions to consumer psychology and offers a set of criteria that are appropriate and inappropriate for adjudicating qualitative research of this kind. In all, we make the case that the conditions are in place for JCP to be a vibrant platform for publishing research based on qualitative methods.
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 | 0.539 | 0.512 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.086 |
| Scholarly communication | 0.031 | 0.058 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.023 | 0.036 |
| Insufficient payload (model declined to judge) | 0.014 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".