Social Media Networks and the “Unthinkable Present”: A Users’ Perspective
Bibliographic record
Abstract
A decade ago the Canadian author William Gibson observed that science fiction is often mistakenly credited with predicting the future, simply because technological change seems to happen so quickly. With the benefit of hindsight, he argues, observations of emerging trends can only seem prescient if they are not interrogated too deeply: “As I’ve said many times before the future is already here, it’s just not very evenly distributed” [1]. What we perceive as new technology is often a combination or application of current but hitherto distributed knowledge or tools-for example, the relatively rapid development of smartphones and tablet computers can be attributed to many decades of prior development in telecommunications, computing and even photography and satellite navigation.What we have seen in the first decade of the 21st century is a coming together of existing social and computing networks to form new patterns of connections in the online world. These principles of human social interaction, painstakingly unearthed in the past by social scientists using small sample sizes and in-depth field research, are now becoming available for empirical research in an unprecedented way.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".