Building a better literature review: Looking at the nomological network of the country‐of‐origin effect
Bibliographic record
Abstract
Abstract Due to a rising pace of knowledge production, reviewing extant knowledge on mature topics has become increasingly challenging. Researchers often need to account for hundreds of references with little guidance on how to proceed. Taking the phenomenon of the country‐of‐origin effect (COE) as an example, this paper proposes a solution to tackle this challenge. By adopting the principles of integrative literature reviews and using online databases, bibliography management software, and literature‐mapping techniques, I organize 355 papers about the COE. As a result, the nomological network of the COE is drawn while establishing links between the phenomenon, its antecedents, and its outcomes. This methodological article contributes to building better literature reviews. Copyright © 2015 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.070 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.066 | 0.051 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".