Surfacing Human Service Organizations’ Data Use Practices: Toward a Critical Performance Measurement Framework
Bibliographic record
Abstract
Community-level data systems, often called collective impact, increasingly define the landscape of human service data creation. Collective impact strategies develop shared performance measurement metrics across numerous human service organizations (HSOs) in a geographic region to move the needle on specific social problems. Such systems encourage funders to support the development of client tracking and data sharing infrastructure, meaning more HSOs have more information about any given client. However, while many HSOs are using more data than ever, questions remain: how is this data being read, understood, and utilized in HSOs? What differences can we discern in organizational operation and service provision? This study builds on three years of participant observation as program evaluators in youth-serving organizations (a subtype of HSOs) around the world. It also included a national study of youth-serving organizations with a strong focus on data use. Finally, it includes interviews with program staff in youth-serving organizations and focus group data with young people. Situating this data between the literature on performance measurement in HSOs and critical data studies, we surface emerging tensions in the ways youth-serving organizations are creating and using data, drawing to the fore salient questions for those invested in supporting the just use of data and technology for our communities.
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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 | 0.337 | 0.302 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.046 | 0.022 |
| Science and technology studies | 0.019 | 0.122 |
| Scholarly communication | 0.051 | 0.060 |
| Open science | 0.010 | 0.029 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".