Bibliographic record
Abstract
The era of Big Data has significantly transformed the state structure and its mechanisms, particularly in relation to how power is exercised and how social governance is approached. While James C. Scott's analysis in the late 20th century critiqued the high modernist state for simplifying and standardizing systems for better manageability and predictability, today's dataist state operates with a radically different paradigm. This transformation is deeply influenced by the rise of distributed data systems that aim to understand the society, individuals, and reality through continuous streams of data. This paper examines the ways in which these data flows reshape the state's vision of governance, focusing on three primary behaviors: simplification, standardization, and homogenization in the context of the dataist state's management of data. Furthermore, it delves into the shift from traditional state control to a system of automated governance where algorithms and machine learning play key roles in decision-making processes. Special attention is given to the concept of "metis"-a form of knowledge deeply rooted in experience and tacit understanding-highlighting the limitations and exclusions that arise in the data-driven governance model. The paper also explores the ethical and social implications of the growing dependence on automated systems, such as the risks of reinforcing biases and exclusions through data processing, ultimately questioning the capacity of dataist states to capture the complexities of human existence. Through these inquiries, the paper presents a critical perspective on the evolving role of the state in an increasingly digital world.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.023 | 0.040 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".