Bibliographic record
Abstract
ABSTRACTObjectivesThe Social Data Linkage Environment (SDLE) at Statistics Canada promotes the innovative use of existing administrative and survey data to address important research questions and inform socio-economic policy through record linkage. It expands the potential of data integration across multiple domains, such as health, justice, education and income, through the creation of linked analytical data files without the need to collect additional data from Canadians.ApproachAt the core of the SDLE is a Derived Record Depository (DRD), essentially a national dynamic relational data base containing only basic personal identifiers. The DRD is created by linking selected Statistics Canada source index files for the purpose of producing a list of unique individuals. These files are brought into the environment, processed and linked only once to the DRD. Each individual in the DRD is assigned an SDLE identifier. Some of the source index files used to build the DRD include tax records, vital statistics registration records (births and deaths), and immigrant data. Updates to these data files are linked to the DRD on an ongoing basis. Only basic personal identifiers are stored in the DRD. Examples of personal identifiers stored in the DRD include surnames, given names, date of birth, sex, insurance numbers, parents' names, marital status, addresses (including postal codes), telephone numbers, immigration date, emigration date and date of death. The paired SDLE identifiers and source index file record IDs resulting from the record linkage are stored in a Key Registry. To reduce the risk of privacy intrusiveness and to minimize the risk of disclosure, source files are separated into source index files and source data files. Employees performing the record linkages in SDLE have access to only the basic personal identifiers needed for linkage. Employees who build the analytical files for research have access only to the data stripped of personal identifiers.ResultsThe SDLE is a highly secure environment that facilitates the creation of linked population data files for social analysis. It is not a large integrated data base.ConclusionThe SDLE program facilitates pan-Canadian social and economic statistical research. It is a record linkage environment that: increases the relevance of existing surveys without collecting new data; substantially increases the use of administrative data; generates new information without additional data collection; maintains the highest privacy and data security standards; and promotes a standardized approach to record linkage processes and methods.
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 | 0.066 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.038 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.230 | 0.117 |
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".