Consequences of the Internet for self and society : is social life being transformed?
Bibliographic record
Abstract
Part I: Introduction:1. Introduction to the issue: John A. Bargh, Department of Psychology, New York University.Part II: The Internet and the Individual:2. Relationship Formation on the Internet: What's the Big Attraction?: Katelyn Y. A. McKenna, Amie S. Green, & Marci E. J. Gleason, Department of Psychology, New York University.3. Can You See the Real Me? Activation and Expression of the 'True self' on the Internet: John A. Bargh, Katelyn Y. A. McKenna, & Grainne M. Fitzsimons, Department of Psychology, New York University.4. Internet Paradox Revisited: Robert Kraut, Sara Kiesler, Bonka Boneva, Jonathon Cummings, Vicki Helgeson, & Anne Crawford, Department of Human-Computer.Interaction, Carnegie-Mellon University.5. Internet Use and Well-Being in Adolescence: Elisheva F. Gross, Jaana Juvonen, & Shelly L. Gable, Department of Psychology, University of California - Los Angeles.Part III: The Internet and the Organization:6. When are Net Effects Gross Products? The Power of Influence and the Influence of Power in Computer-Mediated Communication: Russell Spears & Tom Postmes, Department of Social Psychology, University of Amsterdam Martin Lea, Department of Psychology, Manchester University Anka Wolbert, Department of Social Psychology, University of Amsterdam.7. Negotiating via Information Technology: Theory and Application: Leigh Thompson, Kellogg Graduate School of Business, Northwestern University, Janice Nadler, Northwestern University and American Bar Foundation.Part IV: The Internet and Government:8. Civic Culture Meets the Digital Divide: The Role of Community: Electronic Networks: Eugene Borgida, John L. Sullivan, Alina Oxendine, Melinda S. Jackson, Eric Riedel, & Amy Gangl, Departments of Law and Psychology, University of Minnesota.9. Dark Guests and Great Firewalls: The Internet and Chinese Security Policy: Ronald J. Deibert, Department of Political Science, University of Toronto.Part V: Methodological Techniques and Issues:10. eResearch: Ethics, Security, Design, and Control in Psychological Research on the Internet: Brian Nosek & Mahzarin R. Banaji, Department of Psychology, Yale University, Anthony G. Greenwald, Department of Psychology, University of Washington.11. Studying Hate Crime with the Internet: What Makes Racists Advocate Racial Violence? Jack Glaser & Jay Dixit, Goldman School of Public Policy, University of California - Berkeley Donald Green, Department of Political Science, Yale University.Part VI: Concluding Perspective:12. Is the Internet Changing Social Life? It Seems the More Things Change, the More They Stay the Same: Tom R. Tyler: Department of Psychology, New York University.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".