Bibliographic record
Abstract
There is a global danger that hides in the shadows and is changing the way continuing care operators care for residents. Today, to provide that same historical level of resident care, a group of techno-soldiers are not just a nice-to-have, but are a must, to protect our critical Information Technology (IT) infrastructure. There is little doubt that technology has enabled us to provide better care. Electronic medical records, human resources information systems, and voice over Internet communications systems have created great efficiencies in operations. However, global cybercriminals lurk in the same technologies that support us. Continuously attempting to rob our private information and hold us ransom, or play havoc with our IT systems impeding our provision of care. How can a continuing care operator, an already funding-challenged industry balance the costs of protecting their IT infrastructure against this growing massive threat? Compounding the issue, rising insurance expectations for continuing care operators to continuously enhance their IT safety controls against evolving cyberterrorism. This is the story of the Good Samaritan Society/Canada (Good Samaritan) digital transformation journey. Our digital roadmap sets Good Samaritan up to rise above the ongoing barrage of cyberattacks and loss of insurance. This is a digital divide story of David vs. Goliath.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".