Bibliographic record
Abstract
Existing in virtual global networks poses questions on how subjects can main- tain agency within them. The shift of digital networks into compartmentalized locked-in platforms facilitates the metamorphosis of subjects into the category of users. The ever-growing absence of a buffer zone between online and offline representation means that online highjacks become increasingly problematic. Currently, there is a rise in emerging efforts to transition bureaucratic citizenship into new modes of digital identity, such as the ID2020 project and other govern- mental projects seeking to implement digital ID in countries like Australia and Canada, or the digital ID proposed by the World Economic Forum. This shift, in some cases from a centralized state as guarantor to a decentralized allocation of identity allows easy verifiability and access to a multiplicity of services and personal data but also poses questions concerning these systems. This research aims to look at the different models of digital identity that are being developed, thinking about their implications. Researching in an art context, the goal is to develop a speculative project related with digital ID and expropriation, reflecting upon the assetisation of identity. The goal is to think through different theories to explore agency within virtual global networks distributed in planetary-scale assemblages of subjects and technological infrastructures.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".