Bibliographic record
Abstract
Organisations are going to great lengths to protect the data on their networks but many are falling at the final hurdle by failing to delete data properly from end-of-life equipment. Part of the problem appears to be that, on the face of it, it seems easy to delete data securely. But all too often the data proves to be recoverable. Tracey Caldwell explains how a lifecycle approach to managing data can identify data and plan for its proper and efficient destruction. Organisations are going to great lengths to protect data on their networks. But many are falling at the final hurdle by failing to delete data properly from end-of-life equipment. This leaves them vulnerable – not just to the leak of sensitive information, but also potential fines and other legal ramifications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.049 |
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".