Introduction to the special section on the study of social withdrawal during childhood and adolescence: In honor of Dr. Kenneth H. Rubin
Bibliographic record
Abstract
This special section is in honor of Dr. Kenneth H. Rubin and his unparalleled intellectual contribution to the study of the development and implications of social withdrawal. It also recognizes his almost 50 years of enthusiastic and devoted mentorship and international collaboration, which has stimulated numerous scholars around the world to investigate social withdrawal-related topics. The six contributions in this special section are written by scholars from diverse perspectives and backgrounds and include longitudinal datasets from four different countries. This introduction highlights the empirical contributions of these studies, characterized into three broad categories that have been central to Ken’s enormously influential work on social withdrawal: (1) peers as contributors to, and outcomes of, social withdrawal; (2) the consideration of the diverse contexts in which social withdrawal can occur; and (3) social withdrawal as a modifiable risk factor. Together, these studies extend knowledge about social withdrawal and its concomitants, set the stage for future research and intervention efforts, and honor the legacy of Dr. Kenneth H. Rubin.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".