TA 2: Final Report: Geospatial Data Archiving and Preservation
Bibliographic record
Abstract
This report is written to meet the following objective: conduct research and provide analysis and recommendations on issue of archiving and preserving CGDI’s geospatial data assets, including solutions for perpetual access. The report is based primarily on research of available documents and literature, supplemented by consultations with a primary stakeholder, Library and Archives Canada (LAC). The report is structured in nine chapters, including this introduction. The second chapter provides a synopsis and analysis of some of the more significant legislative requirements for the archiving and preservation of Canada’s geospatial data. The third chapter reports on the consultation with Library and Archives Canada concerning their guidelines and best practices that might be used across government. Chapter 4 assesses geospatial considerations in Canada’s current information management policy, highlighting key Treasury Board policies and directives. The fifth and sixth chapters report the results of an environmental scan of Canadian consultations and reports and Canadian research on digital data archiving and preservation, respectively. Chapter 7 identifies some best practices based on an environmental scan of geospatial data archiving and preservation in Canada and abroad. The eighth chapter identifies a few business models for archiving and preservation, especially those that promote cooperation and sharing responsibility and costs for preservation activities. The final chapter provides conclusions drawn from the research findings and analysis and recommendations for follow up action.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.047 |
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".