Bibliographic record
Abstract
IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward.
 Objectives and ApproachOur objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper.
 ResultsWe define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary “team science” field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement.
 Conclusion/ImplicationsThese implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research.
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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 | 0.036 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".