Trusts in Business Research: A Concise Systematic Literature Review
Bibliographic record
Abstract
This study is a concise, systematic literature review of a legal instrument called the ‘trust’ in academic business research. Therefore, it aims to define the state-of-the-art and future research paths on this topic. Web of Science and Scopus are bibliographic databases used to collect data, and a rigorous and transparent methodological protocol has been used to conduct a systematic literature review. The study of bibliometric data and the content analysis of the research sample allow for insightful considerations, showing the main business areas dealing with trusts (the financial, historical, and corporate governance areas) and mapping the specific sub-topics of each one. Moreover, a new trend outlines the increasing interest in trusts and connected institutions (investment trusts, trust companies, unit trust funds, real estate investment trusts) from the traditional UK and US contexts to new emerging countries such as China, Malaysia, and South Africa. In conclusion, many gaps in the literature and opportunities for future business research are outlined. This study helps overcome the traditional juridical study perspective of trusts, showing many links with businesses and corporations and encouraging scholars to investigate the trusts in the business world.
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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 | 0.050 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.045 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".