Threats to Future Knowledge: The Impact of the Pandemic on Organisational Recordkeeping
Bibliographic record
Abstract
This paper reports the outcomes from the first phase of an international research project investigating the impact of the COVID-19 pandemic on organisational recordkeeping. Recordkeeping is a critical component of organisational knowledge management, as the making and keeping of records as evidence of organisational activities and transactions enables core memory and accountability functions over time. Working from home during the pandemic has disrupted routines of records creation, storage, and management, and will likely result in substantial black holes in future knowledge. The objective of the first phase of our study was to find out what records-related initiatives were underway in academic settings and in archival institutions in the initial stages of this global crisis. We conducted an environmental scan, which showed that much attention was being paid to documenting the pandemic (e.g., collecting and preserving social media discussion, promoting the use of diaries by citizens); however, the provision of advice and standards for organisational recordkeeping at a time when regular access to organisational systems could not be guaranteed was largely missing. In the second phase, we designed a survey aimed at capturing the experiences of recordkeeping professionals who worked from home for varying lengths of time in Europe, North America, and Australasia. It is expected that this comparative study will help us envision a “new normal” for the time when the current health emergency is over. This paper concludes with a discussion of how our environmental scan and literature review have informed the multilingual survey that is currently underway.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".