Bibliographic record
Abstract
Purpose This study aims to identify Canadian archives that are at risk for climate change threats, to present a snapshot of current practices around disaster planning, sustainability and climate adaptation and to provide recommended next steps for records managers and archivists adapting to climate change. Design/methodology/approach These objectives were achieved by analyzing the geographic locations of Canadian archives in relation to projected climate data and by analyzing the results of a survey distributed to staff at Canadian archival repositories. Findings This study found that all Canadian archives will be impacted by projected changes in both annual mean temperatures and precipitation to the year 2080. Themes that emerged surrounding climate adaptation strategies include the investment in the design and efficiency of spaces housing records and the importance of resilient buildings, the need for increased training on climate change, engaging senior leadership and administrators on climate change and developing regional strategies. Preparing for and mitigating the impact of climate change on the facilities and holdings needs to become a priority. Originality/value This research underscores the importance of developing climate adaptation strategies, considering the sustainability of records management and archival professional practice, increasing the resilience of the facilities and records and strengthening the disaster planning and recovery methods.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".