Bots, Babes and the Californication of Commerce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines the recent trend in automated electronic commerce to animate avatars and other electronic entities and use them to build relationships with consumers through the illusion of friendship. The author argues that further research and writing is needed on this neglected subject, and that new approaches to privacy and consumer protection are required to ensure that the interests of everyday consumers are not exploited by they web's wide world of bots and babes. The author begins his analysis with a discussion of the law of contract as it applies in the context of automation. Once the contractual foundations have been laid, his focus turns to the technologies that automate electronic commerce. Here, his primary objective is to trace the architectures of human-computer interaction (HCI) back to their conceptual origins within the field of artificial intelligence (AI). By examining the AI techniques employed to automate and animate electronic commerce, the author exposes some of the trickery used to deceive consumers, a disturbing trend which is referred to as the californication of commerce. Vendors of online goods or services use avatars, shopping bots, vReps, or digital buddies as the primary source of information during the negotiation and formation of a contract. These electronic entities are used to simulate familiarity and companionship in order to create the illusion of friendship. Such illusions can be exploited to misdirect consumers, the net effect of which is to diminish consumers' ability to make informed choices and to undermine the consent principle in data protection and privacy law. The author questions whether our lawmakers ought to respond by enacting laws more robust than those stipulated in today's typical electronic commerce legislation which, for the most part, tend to be limited to issues of form and formation. The author concludes by foreshadowing an important set of concerns lurking in the penumbras of our near future, and demonstrating that some persons are in need of legal protection right now - protection not from intelligent machine entities but, rather, from the manner in which some people are using them.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it