Bibliographic record
Abstract
We live in a surprisingly violent world. We experience physical assault, emotional and psychological attack, and even the intellectual violence of manipulation and indoctrination. Psychology is becoming increasingly aware not only of the prevalent violence, but also of the profoundly deleterious impact of said violence on the human mind and body; however, sociology lags behind. Socialization, or our experiences at the hands of agents of socialization, is a key concept in sociology. Every introductory sociology text that is printed devotes and entire chapter to a discussion of socialization and related concepts. However rarely, if at all, is there any indication that sociologists are aware of the profoundly deleterious impact of toxicity (violence, neglect, etc.) in the socialization process. This research note seeks to alleviate this lacuna by providing a concept, toxic socialization, by which sociologists and others (e.g., psychologists, parents, teachers, and anybody involved in the socialization of human beings) can more readily discuss the problem of a violence and neglect in our socialization processes.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".