Stock Price Reaction to Merger and Acquisition Announcements in Canada
Bibliographic record
Abstract
Practitioners and researchers have long been challenged with identifying deceptive response styles in forensic contexts, particularly when differentiating malingering from factitious presentations. The origins and the development of factitious disorders as a diagnostic classification are discussed, as well as the many challenges and limitations present with the current diagnostic conceptualization. As an alternative to a formal diagnosis, forensic practitioners may choose to consider most factitious psychological presentations (FPPs) as a dimensional construct that are classified like malingering as a V code. Building on Rogers' central motivations for malingering, the current article provides four explanatory models for FPPs; three of these parallel malingering (pathogenic, criminological, and adaptational) but differ in their central features. In addition, the nurturance model stresses how patients with FPPs attempt to use their relationship with treating professionals to fulfill their unmet psychological needs. Relying on these models, practical guidelines are recommended for evaluating FPPs in a forensic context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".