Lingo-Entrainment: The Natural Language Surveillance Of Smartphone Users
Bibliographic record
Abstract
<div><br></div><div>This thesis examines the neuro-cultural implications of: (1) language capture and commodification; and (2) neurological entrainment, two processes that I contend have coextensively emerged with the development of smartphones in a way that is profitable for major smartphone manufacturers and privileged third parties. The phrase “neurological entrainment” in this context refers specifically to the smartphone’s ability to exert affective behavioural control over smartphone users by altering their neurochemical states. I aim to situate this established neurological phenomenon alongside a less scrutinized transformation: that of the smartphone into a site of language-capture. By “language capture” I refer to the intake, collection and brokering of smartphone users’ natural language data and metadata. The goal of this thesis is to contextualize the interfusing of these entrainment and capture processes that cunningly form lucrative linguistic relationships between smartphone users and their devices. This study, through a comparative content analysis of data policies, privacy protocols, and privacy related promotional material pertaining to two major smartphone manufacturers (e.g., Apple and Samsung), substantiates the claim that the foundational documents of each device openly permit this productive union, with its doubled effect of neurological pacification and linguistic divestment. divestment. It also situates these findings within the grander lingo-entrainment systems that influence the future of our living language, and that coincide with Deleuzian premonitions about societies of control.<br></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".