Strategi Public Relations Mandalika Grand Prix Association (MGPA) dalam Manajemen Event World Superbike 2022
Bibliographic record
Abstract
Advances in technology and the digitalization of information have resulted in the media convergence movement growing and technological advances becoming more rapid. In today's media convergence era, there are various options in event management. This study aims to find out how the role of the Mandalika Grand Prix Association (MGPA) in the 2022 World Superbike event in Mandalika amidst media convergence. More than 50 thousand spectators came to watch the 2022 WSBK in Mandalika. This figure exceeded the initial target of 45 thousand viewers. One of the aspects that made the WSBK 2022 event successful was the event promotion strategy that was able to bring in tens of thousands of spectators. This success is inseparable from the role of Public Relations in branding and making events attractive. Public Relations must be observant in packaging events so as not to generate risks and issues in the community. This research is a constructivist paradigm and uses a descriptive qualitative approach, using the Ronald D. Smith model which analyzes the Public Relations strategy through 4 stages that identify 9 steps in planning public relations activities. From the research results, it was found that MGPA's Public Relations succeeded in utilizing technological advances as a means of event management to become more effective. able to build the image of World Superbike 2022 to become an event that is trusted by the community and its stakeholders. MGPA's Public Relations has succeeded in carrying out four PR roles in building the company's brand image, namely: (1) as a communicator for company stakeholders; (2) making publications; (3) carrying out Corporate Social Responsibility (CSR) activities; and (4) building the company's image program.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".