Bibliographic record
Abstract
Introduction The rise and development of online technology in the last several years has reshaped politics and policy making in many ways (Leighninger, 2011: 20). In the United States, President Obama used social media applications such as Facebook, Twitter, MySpace, and YouTube during his presidential campaign in 2008 and 2012. In Arab countries, many activists who played crucial roles in the Arab Spring used social networking as a key tool in expressing their thoughts concerning unjust acts committed by governments. In Canada, economists use Twitter to engage with the public, journalists, politicians, and each other regarding their opinions of public policies (Jeff, 2011). These new forms of online communication and social networking tools are sometimes referred to as Web 2.0 (Anttiroiko, 2010: 18) or social media applications. Although the names (social media, social networking tools, Web 2.0, and so on) are different, they all refer to the use of web-based technologies to create highly interactive platforms through which individuals and communities share, cocreate, discuss, and modify user-generated content, such as Facebook, Twitter, blogs, and online forums. These applications offer numerous communication, information, and public relations benefits to individuals and organizations (Cain, 2011: 1036). The interactive characteristic of social media allows governments and citizens to exchange information and ideas in a click at almost no cost. However, the government has to be alert to the impact of social media because these new, powerful communication tools are capable of influencing users’ opinions in the realms of politics and policy (Auer, 2011: 709). Nowadays, it is common for firsthand information to be released not in the traditional media but in private blogs, on Youtube or through other social media tools. For instance, during the 2004 Indian Ocean earthquake and tsunami, a Singapore resident, Rick Von Feldt, who witnessed the tsunami while holidaying on the beach in Phuket, Thailand created a blog detailing his survival experience. In Taiwan, an increasing number of drivers upload videos taped by their GPS devices of car accidents or robberies. When people use social media tools to share their own experiences using words or photos of public events such as disasters without any pre-screening, this sharing can possibly cause unexpected panic if the information contains personal bias or emotion.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".