Troubling Neurobiological Vulnerability: Psychiatric Risk and the Adverse Milieu in Environmental Epigenetics Research
Bibliographic record
Abstract
In post-genomic science, the development of etiological models of neurobiological vulnerability to psychiatric risk has expanded exponentially in recent decades, particularly since the neuromolecular and biosocial turns in basic research. Among this research is that of McGill Group for Suicide Studies (MGSS) whose work centers on the identification of major risk factors and epigenetic traits that help to identify a specific profile of vulnerability to psychiatric conditions (e.g., depression) and predict high-risk behaviors (e.g., suicidality). Although the MGSS has attracted attention for its environmental epigenetic models of suicide risk over the years and the translation of findings from rodent studies into human populations, its overall agenda includes multiple research axes, ranging from retrospective studies to clinical and epidemiological research. Common to these research axes is a concern with the long-term effects of adverse experiences on maladaptive trajectories and negative mental health outcomes. As these findings converge with post-genomic understandings of health and also translate into new orientations in global public health, our article queries the ways in which neurobiological vulnerability is traced, measured, and profiled in environmental epigenetics and in the MGSS research. Inspired by the philosophy of Georges Canguilhem and by literature from the social studies of risk and critical public health, we explore how the epigenetic models of neurobiological vulnerability tie into a particular way of thinking about the normal, the pathological, and the milieu in terms of risk. Through this exploration, we examine how early life adversity (ELA) and neurobiological vulnerability are localized and materialized in those emerging models while also considering their broader conceptual and translational implications in the contexts of mental health and global public health interventions. In particular, we consider how narratives of maladaptive trajectories and vulnerable selves who are at risk of harm might stand in as a "new pathological" with healthy trajectories and resilient selves being potentially equated with a "new normal" way of living in the face of adversity. By troubling neurobiological vulnerability as a universal biosocial condition, we suggest that an ecosocial perspective may help us to think differently about the dynamics of mental health and distress in the adverse milieu.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".