Bibliographic record
Abstract
Building the InfrastructureAs Korean society modernizes, social work finds its place and social work education continues to grow > Bearing WitnessStudies show that adolescents' exposure to community violence can cause trauma and lead to destructive behaviors.So why aren't we doing more about it? > A Longitudinal StudyDodie Norton's research of a group of South Side children has been a landmark in understanding how parental interaction impacts childhood development features V O L U M E 1 4 | I S S U E 1 | S P R I N G 2 0 0 7 2 > viewpoint: from the dean Engagement and Eminence 4 > conversation Baby Talk 6 > ideas Charting a New Course With an ambitious mapping project, researchers are learning about health on the South Side and building a new community asset No Cost Care Free clinics fill the gap in health care for the uninsured Foreclosure Relief How much is enough? 8 > inside social service review Staying Safe Women's strategy to avoid sexual violence at college parties is to be careful-rather than demanding a safe environment Access Denied Trouble getting services without proper ID 9 > a voice from the field Translating Transitions 44 > behind the numbers Shrouding Violence Why do relatively few youth talk about witnessing an act of violence outside the home?departments
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.398 | 0.279 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".