Raw data or hypersymbols? Meaning-making with digital data, between discursive processes and machinic procedures
Bibliographic record
Abstract
Abstract The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production and circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.102 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".