Bibliographic record
Abstract
People experience and treat medication as though it were a person: in other words, as an object. Among the many symbolic meanings attributed to medication, this sort of personification, or object representation, is a meaning that medication is uniquely positioned to contain and convey: imbued with intentionality and influence, medication moves beyond the sphere of static, iconic representation and enters the changeable, dynamic object world of action, aim, and agency. Unlike more generic or stereotypic meanings, object representations attributed to medication may reflect the patient's specific dynamics and object relations. These representations are many and mutable, and take on shifting and overlapping forms that evolve with the analytic process. Medication may represent a third person within the framework of an analytic treatment, expanding the analytic dyad into a triad and offering new transference paradigms to explore. The defensive displacement of transferential qualities and attitudes, or split-off parts thereof, from the analyst onto medication can serve as a powerful resistance to the awareness of the transference to the analyst. Clinical examples illustrate the utility and importance of the analysis of medication as object, for both patient and analyst, with particular attention to the transference.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".