“AI-Human Romance,” or, The Deconstruction of Love: A Baudrillardian Discourse Analysis of Hyperreality
Bibliographic record
Abstract
The digital revolution has partly disembodied humanity, as evermore time and effort are spent on cyberspace, where virtual simulations of the real increasingly preoccupy our minds. The AI is the last simulator joining the party, now also simulating “love” and “romance.” Today, millions of people are entering “love relationships” with the algorithm. Using a discourse analysis and insights on hyperreality from Jean Baudrillard, Mark Slouka, and others, I analyze how “AI-human relationships” are discursively constructed, and how this discourse establishes hyperreality, as the AI and its simulation of intimacy are represented as equivalent to human partners and real love. Love, the codification of intimacy, is deconstructed by this discourse, as the idea of “AI-human love” displaces intimacy and other signs we associate with love, essentially transforming love into the codification of satisfaction, unrelated to the human touch. La révolution numérique a en partie désincarné l'humanité, car de plus en plus de temps et d'efforts sont consacrés au cyberespace, où les simulations virtuelles du réel occupent de plus en plus nos esprits. L'IA est le dernier simulateur à rejoindre la fête, simulant désormais également "l'amour" et la "romance". Aujourd'hui, des millions de personnes entrent dans des "relations amoureuses" avec l'algorithme. En utilisant une analyse du discours et des perspectives sur l'hyperréalité de Jean Baudrillard, Mark Slouka et d'autres, j'analyse comment les "relations IA-humaines" sont construites discursivement, et comment ce discours établit l'hyperréalité, car l'IA et sa simulation de l'intimité sont représentées comme équivalentes aux partenaires humains et à l'amour réel. L'amour, la codification de l'intimité, est déconstruit par ce discours, car l'idée de "l'amour IA-humain" déplace l'intimité et d'autres signes que nous associons à l'amour, transformant essentiellement l'amour en codification de la satisfaction, sans rapport avec le contact humain.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.070 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".