Sponsor and ambush marketing during the 2018 Commonwealth Games on Twitter and Instagram
Bibliographic record
Abstract
Purpose This research explores ambush marketing on social media during the 2018 Commonwealth Games held in Australia. Two social media platforms – Twitter and Instagram – served as the dataset to uncover how official sponsors of the Canadian and Australian Commonwealth Games teams were ambushed. Design/methodology/approach Employing a content analysis of all official team sponsors and their competitors, the study’s findings offer an original and multi-national look into social media ambushing. Findings Results indicated that promoting Games’ links was the most common social media post type used by official event sponsors, followed by sharing results of their endorsed athletes and behind-the-scenes information. Research limitations/implications In an effort to provide connection to the event, posts by ambushers focused on promoting athletes endorsed by their brand. All ambushers were more likely to use Twitter to promote their endorsed athletes. Instagram, however, was not fully embraced in their ambush marketing. Originality/value Discussion and implications of the results provide sport marketers with information on how to leverage one’s link with a major sporting event.
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 | 0.013 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".