Reversing Kristeva’s first instance of abjection: the formation of self reconsidered
Bibliographic record
Abstract
Psychoanalyst Julia Kristeva defines the theoretical concept of abjection as an unconscious defence mechanism used to protect the self against threats to one's subjectivity. Kristeva suggests that the first instance of abjection in an individual's life occurs when the child abjects the mother. However, the instance of abjection addressed within this paper is the reverse of this: the abjection of the child, with a disability, by the parent, and more broadly society. Using the contemporary example of prenatal testing, the authors explore how parents of children with disabilities may be influenced in abjecting the child. The implications of abjection of the child are then used to explore normalization, routinization of care and the development of standardized care practices within health-care. Prenatal screening practices and standardized care permeate medical obstetric care and social discourses regarding pregnancy and childbirth, thereby affecting not only healthcare professionals but also parents in their position as consumers of health-care. In a time when the focus of health-care is increasingly placed on disease prevention and broader medical and social discourses glorify normalcy and consistency, the unconscious abjection of those that do not fit within these standards must be identified and addressed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.084 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".