Nature's memory: an appraisal strategy for Ontario Provincial Park records – Algonquin Park as a case study
Bibliographic record
Abstract
Archival records are essential for understanding changes in the environment. However, these records are largely ignored by archivists, who overwhelmingly focus on records of human history. Algonquin Provincial Park in east-central Ontario was established in 1893 and is the oldest park in the Ontario Ministry of Natural Resources, Provincial Parks system. In addition to the many official functions and activities that occur in Algonquin Provincial Park, it is host to extensive environmental monitoring, assessment and research. However, no adequate system is in place to ensure that the records of archival value are identified, appraised, and preserved. An appraisal methodology is required to assist archivists to identify environmental records of long-term value. Using Algonquin Provincial Park as a case study, through primary source analysis of its structures and functions and comparisons with other park systems, various archival appraisal theories and methodologies are tested in order to determine the best method for appraising environmental records in general and the records of parks. It is determined that macroappraisal is the best method for appraising only the records created by the official functions and activities of Algonquin Provincial Park. However, the park depends on and interacts with many external affiliated organizations, including the Algonquin Forestry Authority, The Friends of Algonquin Park, Algonquins of Golden Lake First Nation, leaseholders, researchers, protest groups, anglers, businesses and contractors. The creation of environmental monitoring, assessment and research records is increasingly accomplished external to official government functions and activities. Many of these external groups create environmental records of archival value, yet, no formal strategy to preserve irreplaceable records is implemented in Algonquin Park. Elements from archival appraisal methodologies such as macroappraisal, the documentation strategy and the Minnesota Method are helpful for determining the relative archival value, and therefore level of documentation, of these external individuals, groups and organizations. This combination of methodologies is the most effective for ensuring the preservation of important environmental information and subsequently the environment. Various preservation partnership strategies are also suggested for these records identified as having archival value.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".