Social Media Networks and the “Unthinkable Present”: A Users’ Perspective
Bibliographic record
Abstract
A decade ago the Canadian author William Gibson observed thatscience fiction is often mistakenly credited with predicting thefuture, simply because technological change seems to happen soquickly. With the benefit of hindsight, he argues, observations ofemerging trends can only seem prescient if they are not interrogatedtoo deeply: “As I’ve said many times before the future is alreadyhere, it’s just not very evenly distributed” [1]. What we perceiveas new technology is often a combination or application of currentbut hitherto distributed knowledge or tools-for example, therelatively rapid development of smartphones and tablet computerscan be attributed to many decades of prior development intelecommunications, computing and even photography and satellitenavigation.What we have seen in the first decade of the 21st centuryis a coming together of existing social and computing networksto form new patterns of connections in the online world. Theseprinciples of human social interaction, painstakingly unearthed inthe past by social scientists using small sample sizes and in-depthfield research, are now becoming available for empirical research inan unprecedented way.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".