Bibliographic record
Abstract
Increased utilization of the Internet and Web 2.0 platforms, such as social media sites, has shifted message control and branding initiatives to include the voice of the consumer. Consumers can engage in two-way communication with brands through posts and comments, which can impact the image and management of brands. As Bal, Campbell and Pitt (2012) acknowledged, “[m]oney and advertising are no longer the sole controllers of message dissemination. Stakeholder interaction is now key to brand and image management” (p. 204). While stakeholder engagement and interactivity now play an integral role in modern-day marketing, the digital media environment simultaneously increases the difficulty of branded initiatives staying on message. In January 2014, McDonald’s, an Olympic TOP sponsor of the 2014 Sochi Olympic Games, launched a social media campaign to activate its sponsorship. The overarching goal of this campaign was to encourage communication and connection between fans and Olympic athletes. Employing two main platforms, Twitter and an official webpage associated with the campaign, individuals could send personalized messages and well wishes to their favourite athletes and teams competing in Sochi through the hashtag #CheersToSochi, or by going to the website www.cheerstosochi.com (McDonald’s, 2014). As part of the activation, six US and Canadian athletes agreed to five-figure endorsement deals with McDonald’s to promote the #CheersToSochi campaign on Twitter and Facebook (Mickle, 2014).
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".