Bibliographic record
Abstract
Elucidating the true structure of depression is necessary if we are to advance our understanding and treatment options. Central to the issue of structure is whether depression represents discrete types or occurs on a continuum. Nature almost universally operates on the basis of continuums, whereas human perception favors discrete categories. This reality might be formalized into a 'continuum principle': natural phenomena tend to occur on a continuum, and any instance of hypothesized discreteness requires unassailable proof. Research evidence for discrete types falls far short of this standard, with most evidence supporting a continuum. However, quantitative variation can yield qualitative differences as an emergent property, fostering the appearance of discreteness. Depression as a continuum is best characterized by duration and severity dimensions, with the latter understood in terms of depressive inhibition. In the absence of some degree of cognitive, emotional, social, and physical inhibition, depression should not be diagnosed. Combining the dimensions of duration and severity provides an optimal way to characterize the quantitative and related qualitative aspects of depression and to describe the overall degree of dysfunction. The presence of other symptom types occurs when anxiety, hypomanic/manic, psychotic, and personality continuums interface with the depression continuum.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".