Bibliographic record
Abstract
Social media use around the word is growing rapidly and governments similar to private sector firms are endeavoring to employ social media for a variety of purposes.Current research (McNutt, 2014;Snead, 2013;Chun & Luna Reyes, 2012;Picazo-Vela et al., 2012;Lavender, 2013; Mergel, 2013b) supports the observation that governments use social media as a tool to provide information to the public, to engage with citizens, to improve service delivery, to enhance democratic engagement, to facilitate feedback and comments from citizens, to communicate with stakeholders and to collaborate across organizations.Although the use of social media may bring benefits to governments, successful deployment of these technologies also faces some challenges and barriers, such as an incompatible organizational culture, information security and privacy issues.While researchers have investigated some of the issues concerning these benefits and barriers, there is still a dearth of empirical evidence related to what citizens and employees, as potential stakeholders, want when interacting with different levels of government through social media tools.This study aims to address two important objectives: 1) understanding social media as a phenomenon and its implications for government use; 2) understanding the perceptions of citizens, employees and government senior management in terms of government use of social media.The fundamental research question for this study is, what is the relative importance of the benefits and barriers linked to social media use by governments for different stakeholder groups?I am also thankful to Dr. Gerald Grant and Dr. Ruth
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.013 |
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".