“To Build a More Just Society”: Women’s National Basketball Association Teams’ Uses of Social Media for Advocacy
Bibliographic record
Abstract
Sports brands and properties are using social media platforms to take a stand on controversial social issues. This paper draws on the concept of corporate social advocacy to examine how Women’s National Basketball Association (WNBA) teams used their social media platforms to communicate about social issues during the 2021 season. We conducted a thematic and semantic analysis of advocacy-related tweets to examine the communicative actions and salient issues across the teams’ accounts. WNBA teams posted about racial justice, women’s empowerment, and LGBTQ+ rights, which represent a shift in the WNBA’s discursive promotional strategies. The findings of the study indicate that WNBA teams’ use of social media to take a stand on social issues aligns with, and extends, conceptualizations of corporate social advocacy. Further, social media advocacy provides insight into the sociocultural significance and the economic viability of women’s sport.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".