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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.039 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".