Innovation for Positive Sustainable Legacy From Mega Sports Events: Virtual Reality as a Tool for Social Inclusion Legacy for Paris 2024 Paralympic Games
Bibliographic record
Abstract
There is significant interest in how sports events and their associated legacies could act as a platform to address global challenges and engender social change. The United Nations (UN) has acknowledged the important role that sport plays in supporting the UN 2030 Agenda for Sustainable Development and the Olympic movement could be argued as central to that objective. Yet critical questions and concerns have been raised about the growing expenditure, viability, long term legacy, and impacts of mega sports events such as the Olympic Games. While much evidence has focused on the challenges of creating legacy for Olympic Games, there is considerably less literature on understanding the Paralympic context. The purpose of this paper is to discuss the role of innovation in creating legacy from MSEs and propose a theoretical and methodological plan for such research. Innovation, a key driver in organizational performance, is suggested as essential to defining, planning for and measuring legacy. We specifically examine the potential of virtual reality (VR) as a technological innovation which can help create a social inclusion legacy in the context of Paris 2024 Olympic/Paralympic Games. A conceptual model is developed, which identifies legacy as a "wicked problem", and this paper discusses the importance of innovation with regards to legacy, by suggesting a new application for VR technology in the context of legacy related to social inclusion. Information technology is a valuable facilitator of social inclusion for individuals with a disability. We specifically examine the potential of VR as a technological innovation which can help create legacy through influencing unconscious biases (symbolic ableism) toward diversity such as disability, gender, and race.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".