1 Social Media Use in State Government: Understanding the Factors Affecting Social Media Strategies in the Minnesota State Departments Professional Paper
Bibliographic record
Abstract
Effective communication is the key in government-citizen relationships, and social media play an important role in “engaging audiences in true multi-way conversations and interactions” (Heldman, Schindelar & Iii, 2013). In the recent years, we have witnessed a growing trend of using social media tools such as Facebook, Twitter, and YouTube in government agencies. The exploding popularity of social media in government agencies enables public relations (PR) practitioners to better reach out diverse audiences and initiate real-time conversation with citizens. From a global perspective, governments in Australia (Alam & Lucas 2011), the Canadian e-government efforts (Small, 2013), and the U.S. Congress (Golbeck, Grimes & Rogers, 2010) have been well studied. In the U.S., most studies have focused on federal-level government but few shed light on state agencies. Current literature revealed that although an increasing number of government agencies have implemented multiple social media tools, strategic communication planning is still a big challenge for social media practitioners in these government agencies. There is limited reflection on strategic planning of engagement activities beyond pushing government information out through social media channels. Interested in studying the social media use at state government departments, this paper
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".