Face threatening messages and attraction in social networking sites: Reconciling strategic self-presentation with negative online perceptions
Bibliographic record
Abstract
"Social Networking and Impression Management: Self-Presentation in the Digital Age, edited by Carolyn Cunningham, offers critical inquiry into how identity is constructed, deconstructed, performed, and perceived on social networking sites (SNSs), such as Facebook, and LinkedIn. The presentation of identity is key to success or failure in the Information Age, especially because SNSs are becoming the dominant form of communication among Internet users. The architecture of SNSs provide opportunities to ask questions such as who am I; what matters to me; and, how do I want others to perceive me? Original research studies in this collection utilize both quantitative and qualitative methods to study a range of issues related to identity management on SNSs including authenticity, professional uses of SNSs, LGBTQ identities, and psychological and cultural impacts. Together, the contributors to this volume draw on current research in the field and offer new theoretical frameworks and research methods to further the conversation on impression management and SNSs, making this text essential for both students and scholars of social media."--Publisher's website.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".