The Domestication of Voice Activated -Technology & EavesMining: Surveillance, Privacy and Gender Relations at Home
Bibliographic record
Abstract
This thesis develops a case study analysis of the Amazon Echo, the first-ever voice-activated smart speaker. The domestication of the devices feminine conversational agent, Alexa, and the integration of its microphone and digital sensor technology in home environments represents a moment of radical change in the domestic sphere. This development is interpreted according to two primary force relations: historical gender patterns of domestic servitude and eavesmining (eavesdropping + datamining) processes of knowledge extraction and analysis. The thesis is framed around three pillars of study that together demonstrate: how routinization with voice-activated technology affects acoustic space and ones experiences of home; how online warm experts initiate a dialogue about the domestication of technology that disregards and ignores Amazons corporate privacy framework; and finally, how the technologys conditions of use silently result in the deployment of ever-intensifying surveillance mechanisms in home environments. Eavesmining processes are beginning to construct a new world of media and surveillance where every spoken word can potentially be heard and recorded, and speaking is inseparable from identification.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".