Bibliographic record
Abstract
Some scholars have noted that an impressive number of self-related terms have been gradually introduced in the scientific literature. Several of these terms are either ill-defined or synonymous, creating confusion, and redundancy. In an effort to minimize this problem, I present a novel and systematic way of looking at possible relations between several key self-terms. I also propose a tentative classification scheme of self-terms as follows: (1) basic terms related to the overall process of self-perception (e.g., self-awareness), (2) non self-terms that are importantly associated to some other self-terms (e.g., consciousness and Theory of Mind), (3) processes related to the executive self and involving agency, volition, and self-control (e.g., self-regulation), and (4) self-views, that is, the content and feelings about the self (e.g., self-esteem). Three additional categories not discussed in this paper are self-biases, reactions to the self, and interpersonal style. Arguably unambiguous definitions for some of the most important and frequently used self-terms are suggested. These are presented in tables meant for the reader to search for definitions as well as related terms.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".