On Arbormosis: Becoming-Cyborg, Machinic Subjection, and the Ethico-Aesthetic of User-Friendly Design
Bibliographic record
Abstract
This paper suggests that the imbrication of user-friendly software and the posthuman has increasingly been revealed as an intrinsically arborescent relationship, one premised upon the striation of personal information through different forms of software media and allowing for unprecedented avenues of control and subjective manipulation. My analysis begins with a conceptualisation of user-friendliness, tracing its development as the majoritarian style of software design. In assessing the effects of this process of subjective imbrication with arborescent software technology, it is suggested that one effect of the pathological asignification of instrumentality in the representation of software is the genesis of a topological space which allows for a capacity for control over the subjective becoming of others through control over digitally mediated perception. To illustrate this point, the revelations concerning Cambridge Analytica's use of targeted advertisements on Facebook to affect voting preferences during the 2016 American presidential election are examined as an example of the quotidian domination which is characteristic of the subjective condition of arbormosis. This analysis can be applied to issues of gender, technology, and modes of subjective control.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.051 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".