Physics and Metaphysics of Post-Truth (Or, Do Realia Deliver us from Artefacts of False Witness?)
Bibliographic record
Abstract
This article outlines one of three systemic conditions underwriting misleading, deceptive, and false content in the socio-digital ecosystem today. Against philosophical realism, which attempts to offer a useful diagnostic bulwark against misinformation, the argument takes up both deliberate and nondeliberate misinformation—from intentional disinformation of all stripes (driven by State, corporate, and other actors), deep fakes, pseudo-science, lies and distorted information to fictions and deceptions unwittingly produced by computational features and functions of machine learning and artificial intelligence (AI), such as with automated prompt-and-response systems. The discussion of misinformation today must additionally reckon with the legal framework guiding online content (and its moderation) as it intersects with the economic incentive structures of contemporary platforms. The structure of the legal economy, in turn, shapes the algorithmic systems and interface designs that curate and generate content on digital platforms. It is the computational feature of the information ecosystem today that is the primary object of consideration in this article.With the increase in computing power in the early 2000s and the archiving of petabytes of digital data, computational approaches shifted from rule-governed, symbolic, and human readable systems to data mining, clustering, and statistical analysis. This statistical turn in the era of the early internet created the conditions for a correlational paradigm for the flow of information within networked communication. The information ecosystem today is ordered by technical tools for prediction and risk reduction—clustering algorithms, Markov chains, n-grams, neural network methods, large language models, and the like. The intersection of the correlational paradigm for computing with the telephonic legal construal of platforms, as well as the concomitant economic incentives, created the conditions for the undermining of documentality imagined by philosophical realism. Following a brief critique of the realist turn in philosophy, the article will explore the correlational paradigm as a technical diagnostic punctum central to the reign of “powers of the false” within the digitally-networked ecosystem today.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".