In Between Companion and Cyborg: The Double Diffracted Being Elsewhere of a Robodog
Bibliographic record
Abstract
Aibo, Sony’s robodog, questions the relations between nature, technology, and society and directs the attention to the difficult and changing triad between machines, humans and animals. Located at the boundaries between entertainment robot, dog, and companion Aibo evokes the question which relationship humans and Aibo can have and which ethical issues are being addressed. Promoted by Sony as a ‘best friend’, it is useful to analyze Aibo within the theoretical framework of feminist philosopher and biologist Donna Haraway, who develops alternative approaches of companionships between humans and dogs. Therefore, I am going to ask how Aibo challenges the understanding of other life forms by humans and how concepts of friendship are at stake. Ethical questions about human perceptions of dogs in the age of doglike robots must be approached. However, Aibo itself follows no predefined category. Aibo does neither live in a merely mechanistic ‘elsewhere’ nor in the ‘elsewhere’ of animals but in an intermediate space, in a doubled diffracted ‘elsewhere’.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".