From Bits to Bodies: Perfect Humans, Bioinformatic Visualizations, and Critical Relationality
Bibliographic record
Abstract
In December 2014, computational biologist Lior Pachter posted the results of his “tongue in cheek” in silico genome experiment on his personal blog, where he declared his discovery that “the perfect human is Puerto Rican.” In this article, I analyze the “perfect human” experiment. I argue that despite the use of 21st-century, cutting-edge technology in computing and genomics, Pachter’s experiment and his use of visualization can be usefully juxtaposed with earlier modes of visualizing heredity, namely the development of composite portraiture in the late-19th century and late-20th century technologies of “morphing.” I temper the celebration of Pachter’s creation of a “mixed race” perfect human in silico with a challenge to its ostensibly progressive stance. I instead suggest that it must be understood in the broader context of eugenic hauntings and contemporary tensions around questions of sex, sexuality, race, nation, and indigeneity. I argue that the scientific, specifically genomic, stories that we tell, can be productively read in light of critiques of biogenetic kinship and the naturalization of heterosexual love. I conclude by arguing that the perfect human experiment makes a particular kind of argument about what it means to be human and perfect and what constitutes legitimate and cognizable modes of relationality.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".