A consensual hallucination no more? The Internet as simulation machine
Bibliographic record
Abstract
In this article, we investigate the macro-role being played – and played out – by digital, social and ‘new’ media today. We suggest that these media, facilitated by the Internet, can together be understood as a vast simulation machine that mediates and modulates everyday life to refashion what was once the ‘real world’ in its own image. Life in the ‘meatspace’ (the physical world) is most valuable, we suggest, not because it involves tweets, opinions or our desires but because these data produce useful and computable digital resources for finance, business and government. Today’s Big Data mining and predictive analytics allow for digital priorities to become non-digital realities, resulting – we suggest – in the algorithmically generated landscapes of today (and tomorrow). The imperatives driving today’s Internet and mobile technology have more to do with making the world computationally comprehensible than with the facilitation of free expression, open markets or open communication. We discuss the conditions created by these digital simulation machines as well as emerging opportunities for subversion and resistance.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".