Bibliographic record
Abstract
This note links three hitherto separate subjects: role-taking, meditation, and theories of emotion, in order to conceptualize the makeup of the self. The idea of role-taking plays a central part in sociological theories of the self. Meditation implies the same process in terms of a deep self able to witness itself. Drama theories also depend upon a deep self that establishes a safe zone for resolving intense emotions. All three approaches imply both a creative deep self and the everyday self (ego) that is largely automated. The creativity of the deep self is illustrated with a real life example: an extraordinary psychotherapy experiment appears to have succeeded because it was based entirely on the intuitions of the therapist. At the other end from intuition, in one of her novels, Virginia Woolf suggested three crucial points about automated thought: incredible speed, role-taking, and by implication, the presence of a deep self. This essay goes on to explain how the ego is repetitive to the extent that it becomes mostly, and in unusual cases, completely automated (as in most dreams and all hallucinations). The rapidity of ordinary discourse and thought usually means that it is superficial, leading to greater and greater dysfunction, and less and less emotion. This idea suggests a new approach to the basis of ‘mental illness’ and of modern alienation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".