The (Neo) institutionalization of legacy and its sustainable governance within the Olympic Movement
Bibliographic record
Abstract
The purpose of this article was to further explore the emergence of legacy and the process through which it becomes a taken-for-granted institutional rule that has impacted how organizations plan and implement the Games. More specifically, this article reviews why and how legacy was adopted, the forces at play, and the subsequent implications on bid and organizing committees and other actors within the Olympic Movement. Institutional theory is applied as a theoretical framework to investigate the emergence and evolution of legacy and its governance. The organizational field under investigation consists of the committees involved in the bidding for and hosting of the Olympic Games, the International Olympic Committee as the main rights holder, and other actors within the Olympic Movement which impact or can be impacted by the event's legacy (e.g., national and international sport organizations and sponsors). Archival material was used as the primary source of data. This source included multiple types of documentation such as bid documents, candidature files, final reports, and related websites. Institutionalization is an ongoing process. As such, in order to further understand the adoption of legacy into the Olympic Movement, the evolution of the concept was broken down into the pre-institutionalization, semi-institutionalization, and full institutionalization phases as described by Tolbert and Zucker. Managerial implications that arise as a result of the institutionalization of legacy and the subsequent objectification of its governance are discussed.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".