“More Human Than Human”: “Flattening of Affect,” Synthetic Humans, and the Social Construction of Maleness
Bibliographic record
Abstract
“More Human Than Human”:“Flattening of Affect,” Synthetic Humans, and the Social Construction of Maleness Jordana Greenblatt We’re all schizophrenic, with defective emotional lives—flattening of affect, it’s called. What I’ve done … that’s become alien to me. In fact everything about me has become unnatural; I’ve become an unnatural self. Philip K. Dick Do Androids Dream of Elecric Sheep? In philip k. dick’s novel, androids are unable to empathize with or feel for others. Their absence of spontaneous emotional response to hypothetical scenarios is the diagnostic used to distinguish them from humans. Androids’ disconnection from others and lack of affective response constitute the “affective flattening” to which Pris, an android, refers, understood in Dick’s novel as a defect that, when unattributable to mental illness, marks one as other than human. Thus, above, Pris lies to her human interlocutor, misattributing the cause of her affective flattening in order to provide a human explanation for the feature used to identify androids, who are subject to being “retired,” a euphemism for their summary execution. In this affective deficit and its implications for human identity, Pris [End Page 41] and her fellow androids (“andys”) are hardly alone. Dick’s novel, Ridley Scott’s film adaptation, Blade Runner, and (writer) Ronald Moore and (producer) David Eick’s recent Battlestar Galactica television series are a few examples of a trend that proliferates from the 1950s onwards.1 In such science-fiction texts, humans build and often come into conflict with beings that have been created by humans or have evolved from those created by humans. These beings are almost indistinguishable from humans, except, of course, for that all-important lack of emotional connection: that necessary flattening of affect. While critics generally frame such texts as questioning the nature of the human,2 they represent specifically gendered anxieties. Synthetic humans are distinguished from “real” humans by a particular form of emotional insufficiency. While they usually do feel, they generally lack abilities to know and understand their feelings, feel in relation to others, and/or empathize with or understand the feelings of others. Commenting on the historical shift in the attributes characterizing synthetic humans, Despina Kakoudaki argues that “If for Descartes … automata differ from people because they have … no reason, by the early twentieth century artificial people differ from real people because all they have is reason” (181). Andys, replicants, and Galactica’s Cylons can all have implanted memories, which means that characters can be synthetic without knowing it. Given that the shift Kakoudaki identifies undermines modernity’s ideal of the human-as-rational-subject, it is unsurprising that the Galactica character who most persistently questions whether or not he is a Cylon [End Page 42] is Gaius Baltar, whom we initially meet as a scientist with an important role in the human Colonies’ defense system and who places great stock in his own rationality. Both Baltar’s investment in rationality and his sex are relevant; given white, Western maleness’s (and normative masculinity’s) association with logic, reason, and denying and flattening affect and femaleness’s (and normative femininity’s) with emotionality, emotional intelligence, and empathy, any sense that emotionality defines the human has gendered implications. Modernity’s rational subject, associated with Enlightenment ideals and the Classical traditions they draw on, is one whose maleness, Westernness, and whiteness has historically been used as the measure against which disenfranchised groups, including women, people of colour, and the colonized, have fallen short, judged insufficiently rational and overly emotional. It is therefore his identity that comes into question when reason is no longer a guarantor of human identity. As Alison Jagger notes, The western tradition has not seen everyone as equally emotional. Instead, reason has been associated with members of dominant political, social, and cultural groups and emotion with members of subordinate groups. Prominent among these subordinate groups in our society are people of color, except for supposedly “inscrutable orientals,” and women. (157)3 As emotion replaces rationality as the metric distinguishing humanity from its synthetic doubles, the science-fiction characters that most question if they are human are the rationalist scientist (Galactica) and the hardboiled man of action (Androids and Blade...
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".