An introduction to the special issue on the past, present and future research on deliberate lookalikes
Bibliographic record
Abstract
Purpose The purpose of this paper is to present the contemporary thinking on deliberate lookalikes and to provide a better understanding of its key forms (counterfeits, copycats and no-name imitations) and markets (deceptive and non-deceptive). Design/methodology/approach This editorial contains a review of current and past literature on deliberate lookalikes along with summaries of all the articles accepted for publication in the special issue on deliberate lookalikes. The guest editors used academic databases such as Web of Science to find the most representative scholarly work on deliberate lookalikes literature. Findings This editorial identifies pertinent research gaps in the literature on deliberate lookalikes. The five selected articles address some of these research gaps and provide useful insights on the purchase and usage of deliberate lookalikes along with directions for future research and ways to apply different research methods that could have important implications for scholars and managers. Originality/value The editorial and special issue extends the knowledge about the deliberate lookalikes and their effects on firms, brands and consumers. This work opens new avenues for the research about different forms and markets in the context of lookalikes.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".