Illuminating Centralized Users in the Social Media Ego Network of Two National Sport Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to examine national sport organizations’ (NSOs’) social networks on Twitter to explore followership between users, thereby illuminating powerful and central actors in a digital environment. Using a stratified, convenience sample, followership between the ego (i.e., NSO) and its alters (i.e., stakeholders) were noted in square, one-mode sociomatrices for the Fencing Canada (381 × 381) and Luge Canada (1026 × 1026) networks on Twitter. Using social network analysis to analyze the data for network density, average ties, Bonacich beta centrality, and core–periphery structure, the results indicate fans, elite athletes, photographers, competing sport organizations, and local clubs are some of the key stakeholders with large amounts of power. Though salient users, such as sponsors and international sport federations, are also present in the network core, NSOs seem better able to increase visibility of their content by targeting smaller scale users. The findings imply managers may wish to reflect upon how these advantaged users can be incorporated into their social communication strategies and how scholarship should continue examining followership as well as content in online settings.
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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 | 0.003 | 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 | 0.000 | 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 it