The past, present and future of mega event legacy. \nAn investigation of the influence of previous mega events on the legacy program of the 2014 Glasgow Commonwealth Games.
Bibliographic record
Abstract
On 9th November 2007 Glasgow won the right to host the 2014 Commonwealth Games. The main agenda of the games was to bring a long lasting legacy within the East End of the city. The area is known as being among the most deprived regions within the UK. Therefore, in significant need of substantial economic, social and physical developments. In times of economic austerity a very popular practice that exists in mega events is host destinations to use mega events to regenerate urban areas that suffer deindustrialization. \nThe aim of this research is to examine whether the practice of current mega events to draw lessons from previous mega events actually exists in the industry. Thus, in order to full-fill the latter aim the case of the 2014 Glasgow Commonwealth Games legacy program will be investigated. The research begins with a brief exploration of the practice mega events to act as a catalyst for urban developments, Glasgow’s background as well as history of the Commonwealth Games. \nThe second part of this research consists a literature analysis which aims at showing the reader academic point of view regarding mega events purpose, aim as well as the potential social, political, economic and political impacts that could arise. Moreover, this part also highlights some good and bad examples previous mega events’ legacy outcomes. \nThen, the thesis proceeds with a justification of why and how the particular research approach, design and data collecting methods were chosen and implemented. Next, a discussion on the relevant findings is presented followed by the positive conclusion that the 2014 Glasgow Commonwealth Games’ organizers and stakeholders did look at previous mega events when designing the legacy program of the Games.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".