Bibliographic record
Abstract
Companies today have a huge task on their hands with the sheer volume of red tape required to demonstrate compliance. Such is the global nature of today's regulations that most organisations must adhere to them even if they are not physically in the markets covered. This is exposing them to greater compliance risk than ever before. Such is the global nature of today's regulations that most organisations must adhere to them even if they are not physically in the markets covered. This is exposing them to greater compliance risk than ever before. Data leaks, intentional or unintentional, happen. And when incidents occur, organisations may find themselves having to prove – both to the authorities and their customers – what measures were taken to secure information. This can leave firms struggling to manage the adverse side effects of data leaks, such as damage to customer loyalty, says Jesse Canada of ASG Technologies.
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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.027 | 0.016 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".