Bibliographic record
Abstract
Virtually seductive qualities of identity sharing, content gratification, and ample social atmosphere have made Facebook the most popular social network, boasting 890 million daily users (“Facebook Reports Fourth Quarter,” 2015; Joinson, 2008; Orchard et al., 2014, Reinecke et al., 2014). Online social network studies largely overlook the individual, limiting the understanding of what exactly drives people to use, abuse, even become dependent on sites like Facebook. Based on the theory of uses and gratifications, Q methodology subjectively observes what draws users to Facebook, focusing specifically on Facebook user characteristics. Past studies neglect the existence of three of the four factor groups discovered in this study, making these effectually new discoveries for academia (Alloway, Runac, Quershi, & Kemp, 2014; Cheung, Chieu & Lee, 2011; Sheldon, 2008, Tosun, 2012; Yang & Brown, 2013). These findings increase understanding of online usage, even addiction, and will help cater future social networks to specific users.
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 | 0.001 | 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 | 0.001 | 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".