Metadata an Integral Part of Statistics Canada Data Quality Framework. Paper prepared for
Bibliographic record
Abstract
Statistics Canada's product is information. The management of quality must therefore play a central role within the overall management of the Agency. The Quality Assurance Framework describes the approaches that Statistics Canada takes to the management of quality. This Framework is based on six indicators: relevance, accuracy, timeliness, accessibility, interpretability and coherence. Metadata are at the heart of the interpretability indicator by providing the information necessary to interpret and utilize the statistics appropriately. The first part of the paper will describe the Quality Assurance Framework and will show how metadata relates to the framework. The second part of the paper will be devoted to metadata. It will include a description of the IMDB, its governance model, the mechanisms put in place to assist managers in loading information and ensuring its coherence, the monitoring of its quality and users ’ access. Another section will outline the minimum set of metadata required to comply with the Policy on Informing Users of Data Quality and Methodology. A final section will focus on the agriculture statistics program. 1
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".