Psychoanalytic notes on the status of depression in curriculum affected by histories of loss
Bibliographic record
Abstract
This paper examines debates about the meaning and value of depression in relationship to efforts to teach about, and learn from, historical loss. It is argued that depression is not solely an individual illness or biological aberration, but a trace and effect of facing the many and profound losses – of culture, language and life – that constitute history. And yet, where there is a tendency to privilege the negative affect of depression as a source of critical insight and remembrance, this paper turns to Andre Green’s (1980 Green, André. 1980. “The Dead Mother.” In Life Narcissism, Death Narcissism, trans. edited by Andrew Weller, 170–200. London: Free Association Books. [Google Scholar]) concept of ‘the dead mother’ to examine the inhibiting effects of depression in the context of the inter-generational relationship between parent and child, and arguably, the teacher and student as well. Using a case study from education, I suggest that depression, while indeed a painful trace of loss, can hinder the capacity to represent and so encounter the sadness, vulnerability and lost omnipotence that history leaves in its wake. I conclude with some thoughts on the conditions needed to narrate the meaning and effects of loss that negative affect alone embodies but cannot yet speak.
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 | 0.006 |
| 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 | 0.041 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".