Bibliographic record
Abstract
Soper’s pain-brain theory, based on evolutionary biology, represents a provocative breakthrough for both suicidology and positive psychology. The main thesis of the book is that the evolved way of choosing to live rather than to die in the face of unbearable suffering is to develop a zest for happiness and meaning. His new theory can be summed up by a two-by-two matrix of “pain-type” versus “brain-type” of reducing pain, and the “keeper” versus “fender” levels of protecting us from suicide. My main critique is that the brain versus pain distinction is confusing because the brain is the center for all the functions needed for to reduce pain and keep us alive. Similarly, his football metaphor of two levels of defense(“keeper” and “fender”) is incomplete because the best defense is offence, when a good last line of defense can be quickly turned into offence. Therefore, a more fluid way of conceptualizing this distinction may be the dialectically interactive dual systems of life protection (Yin) and life expansion strategies (Yang) (Wong, 2012). Soper’s pain-brain theory is similar to Wong’s general existential positive psychology theory of flourishing through suffering (Wong, 2020a, 2021a). Both approaches emphasize the centrality of suffering and posit that whether suffering results in mental illness and suicide or mental health and flourishing depends on whether we have the wisdom of the soul and the necessary social support.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".