Does Stakeholder Engagement Pay Off on Social Media? A Social Capital Perspective
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
Nonprofits use social media to pursue a broad range of mission-related outcomes. Given the centrality of user connections and social networks on these sites, attaining these outcomes is contingent on first generating a stock of online social capital through investing in online relationships. Yet, little is known empirically about this process. To better understand the return on social media, this study develops empirical measures of four key dimensions of social media–based social capital centering on the nature of nonprofits’ network positions and stakeholder ties. The study then tests a series of hypotheses relating the increase in social capital to different types of stakeholder engagement tactics. Using Twitter data on 198 community foundations, the study finds that content with multiple communication cues and intersectoral stakeholder targeting predict higher levels of social capital; communicative and stakeholder diversity, thus, appear to play a key role in the successful organizational use of social media.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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