Data Culture in Social Sector Organizations: Insights from Canada
Bibliographic record
Abstract
The increasing centrality of data, fueled by advancements in artificial intelligence technologies, provides significant opportunities for improving organizational efficiency and effectiveness. However, mission-driven nonprofit organizations in the social sector often encounter challenges in leveraging data for value creation. We are increasingly witnessing the transformation of work across roles and sectors into data-intensive practices. This is especially relevant within the social service sector, where organizations are increasingly turning to data-driven technologies to support evidence-based decision-making amid strained resources available to them (Ekmekcioglu, 2025). As a field, Information Systems has increasingly called for studying the nature, effects, and characteristics of digital data and nuanced the investigating in situ experiences of those who work with data (Xu et al., 2025, Aaltonen and Stelmaszak, 2024). Data culture refers to “an organization’s collective practices mutually shaped by artifacts, beliefs, values, and assumptions that emphasize the use of data to achieve business value” (Schnieders et al., 2024, p.8). This TREO talk seeks to uncover the role of data culture in the context of social services working toward sustainable integration of immigrants and refugees in Canada. Based on a multi-sited study, our findings reveal a set of mediating goals that shape the emerging data culture within social sector organizations in the immigrant settlement service context in Canada. These goals collectively configure the socio-technical infrastructure of data work. We examine how temporal constraints and socio-material conditions influence data practices, highlighting their implications for the production and circulation of knowledge on immigrant settlement. Our analysis reveals unique divergences in how data is understood and utilized (or not) at managerial and frontline levels. Building on these insights, we identify key elements constitutive of data culture in social sector organizations and propose a context-specific definition that reflects the complexities and nuances of data work in mission-driven environments. At the TREO research talk, we will present more detailed data and conclusions toward reimagining design, governance, and relational dynamics of data systems, offering potential avenues for intervention at the intersection of information system design, policy, and practice.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.048 | 0.010 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".