Deleuze and Big Data: How Facebook's Use of Big Data Analytics Shifts Legal Personhood, Privacy and Commercial Expression
Bibliographic record
Abstract
This thesis explores the application of Deleuze to the use of big data analytics by Facebook to conceptualize the fusion between the physical and digital world.The fusion of technology and everyday life revolves around a debate between technological determinism and instrumentalis m.This thesis begins by examining the operation of Facebook as a web 2.0 service and applies a Deleuzian discourse to explore Facebook as an assemblage of control.This assemblage is framed as a soft form of technological determinism in the control of a mass aggregated population of profiles.Facebook's profiles are representative of Deleuze's dividual, a replicated image of the self held in data.The combination of the use of data based surveillance and the data feedback loop in retrieving, analyzing and manipulating these profiles leads to a series of legal challenges.This thesis seeks to provide a framework for understanding these challenges in law.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| 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".