Bibliographic record
Abstract
This chapter uses the idea of the personality system to develop an empirically based description of borderline personality disorder (BPD) organized around the idea introduced earlier that BPD has two main components: the emotional dependency constellation of traits and core self and interpersonal pathology. Borderline Traits The emotional dysregulation cluster of traits may be divided into three groups: emotional, interpersonal, and cognitive (see Table 3.1). Emotional Traits Underlying unstable emotions are two major traits: anxiousness and emotional lability. Other less-common traits, namely, pessimistic-anhedonia and generalized hypersensitivity, also contribute to the clinical picture in some patients. Anxiousness Although most theories emphasize emotional lability, patients with BPD are anxious and fearful. They tend to describe themselves as life-long worriers who see the world as threatening and malevolent and themselves as vulnerable and powerless, which makes them hyper-vigilant for indications of threat, especially loss, rejection, and abandonment. Consequently, emotional crises involve fear, panic-like anxiety, and rumination about painful experiences. The current framework for understanding BPD assumes that anxiousness is the cornerstone trait that influences the expression of other traits. At this point, you may be wondering why I am emphasizing anxiousness when many patients show little overt anxiety and seem more angry than anxious. This is often the case. One reason is that the threat mechanism underlying anxiousness offers the option of responding with either fight or flight. Since anxiety increases feelings of vulnerability, many patients rapidly convert it into anger (fight), which is more tolerable. This occurred with one patient who rushed her young child who had suddenly become very ill to the local emergency room whereupon she immediately quarrelled with staff trying to treat the child. The intense anxiety about the child's safety was overwhelming and hence quickly expressed as anger. It was only later, when discussing the event in therapy, that she acknowledged her fear. This reaction is common in forensic settings where it is important not to appear weak lest this is exploited by others. However, despite the propensity to convert anxiety into rage, it is important to treat the underlying fearfulness by teaching stress management skills and restructuring appraisals of threatening situations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".