Bibliographic record
Abstract
This essay explores the notion that we are all fundamentally shaped by history. I suggest that psychoanalysis needs to attend not just to the history of the patient, but also to the fact that the psychoanalyst and patient alike are affected by history and its traumas. Human experience cannot be separated from the historical and cultural contexts in which we live out our lives. The approach I am describing is not an endorsement of historical determinism, the notion that the past entirely makes us who we are today. It is, rather, a plea to know history so that we might respond to what it can teach us. In making this argument, I draw on the work of the historian and psychoanalyst by training, Thomas Kohut. His work helps us to avoid reductionist views of human experience, whether in the past or the present, and teaches us to use empathy to think and feel our way into the situation of the other person. This is particularly relevant in light of the ongoing prejudices and social injustices in contemporary society.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.075 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".