Exploring Environmental Social Governance in New Luxury-based Ventures in the Context of Michelin Star-rated Restaurants
Bibliographic record
Abstract
The aim of this paper is to explore Environmental Social Governance (ESG) in the emergence of new and luxury-based Michelin star-rated restaurants, but also to provide concrete recommendations for theoretical direction in this uncharted area of scientific business research. While we acknowledge there is an abundance of research around ESG business implementation, we highlight many major gaps within the available research and ultimately recommend a different and much need theoretical framework approach. Our recommended approach gets to the core of the intersection of new luxury ventures and ESG implementation theory. By focusing on Michelin star rated restaurants, less than 5 years in operation, it is here, we deem, is at the very centre of luxury-based entrepreneurship (new luxury venture creation), but also where the necessary theoretical development must be explored. Since the availability and understanding of the research for the intersection of new luxury venture creation and ESG application phenomena is extremely narrow, we consider that only with understanding the underlying motivations for entrepreneurs to implement ESG within the context of luxury-based new ventures, will this allow for the opportunity to further explore even greater research. This will be with a new recommended theoretical framework for ESG within luxury entrepreneurship, both at the national and international levels.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".