Digital Social Services: From Data Aggregation to Culturally Competent Content
Bibliographic record
Abstract
A discussion is provided where various data-intensive efforts of a funded digital social services project are reported on. The goal of the project is to develop a digital library and built-in peer-to-peer support features that serve highly diverse audiences and users of New York State. Current work has already provided a number of insights regarding data aggregation and processing needed for making progress toward culturally competent content for digital social services. Those insights are detailed here, along with planned user-centered evaluations and future data-driven and theoretical research streams. The work described here can raise awareness regarding data requirements for digital social services. On trouvera ici une discussion sur les divers efforts à forte intensité de données d’un projet subventionné de services sociaux numériques. Le but du projet est de développer une bibliothèque numérique et des fonctionnalités de soutien pair à pair intégrées qui desservent des publics et des utilisateurs très divers de l'État de New York. Les travaux en cours ont déjà fourni un certain nombre d'informations sur l'agrégation et le traitement des données nécessaire pour progresser vers des contenus culturellement adaptés pour les services sociaux numériques. Ces informations sont détaillées ici, ainsi que les évaluations planifiées centrées sur l'utilisateur et les futurs flux de recherche théoriques et guidés par les données. Le travail décrit ici peut aider à sensibiliser sur les exigences concernant les données aux fins des services sociaux numériques.
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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 | 0.066 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.028 | 0.028 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".