Bibliographic record
Abstract
This paper explores our relationship to data as a circle of signification and sense. It leads off by describing the grounding epistemic role played by Charles Sanders Peirce's semiotic in establishing data's circle of sense, then goes on to develop an alternative perspective owed to Gilles Deleuze's philosophy of the sign. Leaning heavily on recent secondary literature, the paper spends time explaining how Deleuze, sometimes in concert with Félix Guattari, took up and responded to Peircean semiotics, mutating the latter's thinking in important ways. This prefatory work organizes the original contribution of the paper, which is to ask: how might Deleuze's view of nonsense as it plays a role in his perspective on the circle of sense come to bear on our future relationship to social information systems? Probing the possibility of integrating Deleuze's perspective on nonsense into existing approaches to data, the paper experiments with his event-focused view of signification and sense through a series of interconnected diagrams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".