Bibliographic record
Abstract
Goudey and Bonnin provide an important demonstration of our willingness to accept robots regardless of the degree to which they look like us. This comment seeks to expand their insights in two ways. First, by broadening our conception of what constitutes a robot, I argue that we have already accepted many non-humanoid robots, and that even robotic entities without a visual presence can be compelling and engaging. Second, I suggest expanding the original paper’s psychological treatment of category ambiguity through the anthropological treatment of Mary Douglas. Douglas suggests that category ambiguity is abhorrent because things perceived to transgress categorical boundaries challenge our cultural beliefs and social order. In the case of robots, the beliefs that are challenged are our basic understandings of what makes humans unique and privileged in the world. As machines grow more and more capable, by some accounts they threaten to eclipse and even supplant the human race. I identify several behavioral and ethical research issues that are imperative if we are to deal with and prepare for such possibilities.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.047 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".