Children of the Revolution: Looking Towards a Future of Altruistic and Prosocial Media
Bibliographic record
Abstract
The first generation of social media natives, those who grew up with smartphones and social media, are now coming of age. It may not be incidental that questions probing the broader, weightier, possibly detrimental implications of social media, are beginning to be asked—not just by academics, not just by the public at large, but even by the architects of the phenomenon themselves. New mediums—TV, radio, the Internet—have generally taken approximately a decade from wide-spread availability to mass adoption for the full breadth of their influence, for better or for worse, to come to fruition. We are now at that juncture with social media. This research intends to examine this phenomenon and the disruptions currently taking place, how social media natives fit into this narrative, and what a path towards a more prosocial media might look like. Keeping in mind social media was originally intended to simply digitize social connections, communities and communications, not incite policy change, sway elections, or topple regimes, this research will examine the potential of a technology designed for the former to facilitate the latter, as well as social capital and bonding. Ultimately, this research aims to frame the entry of social media natives into the adult world as part of a paradigm shift and envision how social media with a more intentional, built-in functionality to facilitate altruistic and prosocial actions, in a more tangible fashion, as well as mitigating its capacity to foment malice, might operate.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| 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".