140 Characters or Less: Maintaining Privacy and Publicity in the Age of Social Networking
Bibliographic record
Abstract
Through the advances of technology, people and information are accessible at virtually any time with the touch of a button. The effect of technology’s continuous growth is widespread in the sports world. No longer do you have to wait to get home or find the nearest television to find the score of the game or the latest stats on your favorite player. A cell phone with Internet access can provide you with all of the information you could want, including a way to watch the game live. In fact, technology has gone one step further and allows you direct access to the thoughts of those directly involved with your favorite team through social networking. Social networking sites like Twitter and Facebook allow anyone to express his or her thoughts and actions to friends and followers.1 Additionally, these sites have granted public access to elite athletes and other influential people. The inclusion of celebrities to these sites is fairly new; however, the effects (both good and bad) are starting to reveal themselves. Comments made by professional athletes could be free promotional material for their teams and sport, or they could create a public relations nightmare. This fine line between good and bad publicity via social networking came to light with the 2010 free agency deals of the National Basketball Association (NBA). Before the 2010 NBA Playoffs began, speculation regarding free agency became headline news when Chris Bosh, then starting forward for the Toronto Raptors,2 took to his Twitter page and asked a simple question, “Should I stay or should I go?”3 This tweet created a media firestorm and fan frenzy as people concluded that this statement meant Bosh, in the last year of his
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 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 | 0.005 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".