Bibliographic record
Abstract
IntroductionThe Manitoba Centre for Health Policy has provided international leadership in organizing and accessing administrative databases, linking and analyzing data and translating the findings of research into policy for three decades. During this period, MCHP has addressed numerous challenges in each of these areas. Objectives and ApproachLinked data research is expanding rapidly in terms of access to new data sources, different types of data, sharing of data across jurisdictions, and advances in data analytics. Technical advances such as computing power and artificial intelligence support these developments while governance structures and ethical issues challenge them. This presentation will describe some of the challenges MCHP has met with a view to gaining insight into how solutions evolved and how experience can guide the future of linked data research. ResultsThe scaling up of linked data research will need to address specific challenges including de-identification of free text, accessing and linking data from private enterprise such as wearables, and interdisciplinary collaboration to incorporate new techniques developed by computer scientists. Cross-jurisdictional data analysis presents challenges in addressing differences in data architecture. Inter-jurisdictional and international data sharing create ethical and governance challenges. Experience has demonstrated the critical role that relationship building plays in addressing each of these. These relationships are different depending on the partners. They are all based on the development of common use of language, understanding the motivation and concerns of each party, clearly articulating the benefits of the relationship and data use and attention to the cultural and political environment. Conclusion/ImplicationsLessons from the past can guide us in addressing challenges posed by the exciting opportunities available to us all. While many of these challenges will be solved with technical solutions, we should not overlook the importance of human relationships in building a culture of trust and collaboration as we move
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".