Private Labels and Retail: A Bibliometric Study on Empirical Researches
Bibliographic record
Abstract
This work aims at identifying the characteristics of empirical papers on the issues of private labels and retail, published in national (Brazil) and international journals. The present research is a qualitative descriptive study, which applies a bibliometric analysis, focusing on the works that contribute to the debate on the subjects of private labels and retail, within the scope of marketing. The method to identify scientific works used in this research was the Knowledge Development Process – Constructivist (ProKnow-C). In all, 47 articles were analyzed, 20 national and 27 international. The analyses indicate that papers published in national journals arise mostly from researches conducted in Brazil, Spain is the country in which a higher number of researches on the topic were carried out, the International Journal of Retail & Distribution Management and the Journal of Product & Brand Management have shown the greatest number of published works, Cristina Calvo-Porral is the leading international author (4 papers) and Éderson Luiz Piato is the national leading expert (3 publications). The main sub-topics explored in national works differ from their international conterparts, as well as in the methodological structures used to construct the investigation process. International researches are predominantly quantitative pre-experimental, using questionnaires and document analyses, while national are mostly exploratory, descriptive, using questionnaires and interviews.
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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 | 0.016 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.155 | 0.239 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".