Nobody Wins: The inequitable certification of archival materials as cultural property by the Canadian Cultural Property Export Review Board
Bibliographic record
Abstract
The Canadian Cultural Property Export Review Board (CCPERB) was founded by the Government of Canada in 1977 to establish an idealized framework to value and certify items of significant cultural value. This research explores the CCPERB's time-consuming application process and rigid monetary appraisal criteria to demonstrate how the Board discourages the certification of archival material. Emphasizing the fair market value (FMV) of archives through sales data also places Black, Indigenous, and People of Colour (BIPOC) creators, as well as digital records, at a disadvantage for certification. Reforming CCPERB’s processes requires an open dialogue with archival stakeholders to ensure the equitable certification of all forms of cultural property in Canada.
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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 | 0.086 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.024 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".