Challenges of Self-Presentation and Athlete Branding Among Saudi Female Exercisers: An Auto-ethnography of a Muslim Saudi Personal Trainer Instagram User
Bibliographic record
Abstract
Some athletes have attracted millions of audiences, even if being namely recognised. Cristiano Ronaldo, Neymar JR., and David Beckham have the most Instagram followers on a global scale. Online Social Networks (OSN) allows users to establish their profiles to communicate with others through actions such as follows and comments. Currently, athletes prefer to utilise Instagram for self-branding purposes. Therefore, many studies have examined their practices. From the rising of Model of Athlete Brand Image MABI offline practices to the development of online athlete branding consumers’ engagements on social media, all studies have concerned three main categories to build athlete brand image, namely Athletic performance, Attractive appearance, and Marketable lifestyles. As a Saudi female personal trainer who uses Instagram to build a brand image, this auto-ethnography aims to reflect on my personal experiences, including cultural aspects that affect athlete branding strategies. Athlete branding studies have not focused on cultural differences yet. Most Muslim Saudi women are culturally conservative; they cover their bodies in public as a religious practice. This qualitative study describes my own experiences and Instagram visual content selections. It attempts to understand the motives, outcomes, and online self-presentation challenges and strategies of Muslim female exercisers who aim to build their athlete brand image. A key result indicated that Attractive appearance category was not applicable in the self-presentation of a Muslim female athlete in her athlete branding strategies. The trainer encountered some cultural challenges, for instance, religious values such as veiling and gender segregation, which conflict with the ability to rely on the self-characteristics for branding. Therefore, other strategies were applied, such as presenting body composition before and after test results and testimonials for clients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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".