Bibliographic record
Abstract
This article explores inclusivity as a strategic component of service quality enhancement, corporate social responsibility, and competitiveness in the hospitality industry. The concept of inclusivity is examined in a broad context, encompassing not only physical accessibility but also social, cultural, ethical, and digital dimensions of service delivery. The research highlights the importance of developing customer-oriented environments that are comfortable, safe, and welcoming for all clients-regardless of their physical abilities, sensory impairments, cognitive differences, age, gender identity, or cultural background. The study analyzes international best practices from the United States, Canada, Sweden, Japan, and the United Arab Emirates. It investigates legal frameworks (e.g., ADA, Accessible Canada Act), public strategies (e.g., Dubai Disability Strategy, Swedish Agency for Participation), and corporate initiatives implemented by major hospitality networks such as Marriott, Scandic Hotels, Fairmont, and Atlantis The Palm. These cases demonstrate how inclusivity contributes to brand reputation, customer loyalty, and market expansion. Particular attention is given to the Ukrainian context, where inclusivity is still implemented inconsistently due to regulatory gaps, limited funding, outdated infrastructure, and insufficient staff training. Despite these challenges, examples from Ribas Hotels Group, Reikartz Hotels, and the "Hospitality Without Barriers" initiative reveal positive trends and practical steps toward accessible and inclusive service provision. The article concludes that the integration of inclusive practices must be systemic, involving impact investment, state support, public-private partnerships, and mandatory training for hospitality professionals. Inclusivity is not only an ethical imperative but a critical factor in the sustainable development of the global hospitality sector and the formation of a new culture of interaction in service environments
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.001 | 0.028 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".