The Hunter Hypothesis: Scientific Speculation and Ancestral Imaginaries of Adult ADHD in Chile
Bibliographic record
Abstract
In this paper, I examine the social, subjective, and scientific implications of the hunter hypothesis, an evolutionary etiology for Attention Deficit Hyperactivity Disorder (ADHD). By tracing its presence in Chilean “everyday life” narratives and “neuropsychiatric” discourses, I argue for greater attention to the ancestral imaginaries within evolutionary etiologies. This perspective aims to enhance the study of medical categories as tools for self-understanding and biosociality, to foster interdisciplinary collaboration in mental health research, and to engage with calls for greater participation from neurodiverse individuals and communities. The article is structured in two parts. First, I explore the two-way encounter with the hunter hypothesis, examining its origins and interpretations in both “everyday life” and “neuropsychiatric” Chilean contexts. Second, I analyze the undertheorized role of ancestral imaginaries in scientific and medical speculation, seeking to enrich critiques of evolutionary thinking. I show how other legitimate speculations and testable scientific fictions become possible if we move beyond linear and dichotomic evolutionary narratives. The conclusion emphasizes that engaging with imagination should not involve adopting fixed speculative frameworks. Instead, it calls for democratizing access to scientific speculation to open it up to more nuanced strength-based narratives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".