New security and privacy laws require basic changes in professional practice
Bibliographic record
Abstract
Everybody knows about HIPAA—but what about GLBA? FIPA? The Patriot Act? Homeland Security? NCLB? FCRA? CASB1? PIPEDA? All of these are recent laws that impact acoustical design. Throw in the American Hospital Association/ASHE and AIA’s about-to-be-released ‘‘Guidelines for the Design of Healthcare Facilities’’ as well as the redrafting of DCID 6/9 and it looks like time for careful examination of some professional practices relating to security and privacy. Should INCE members join with and endorse the ASA’s recently formed Joint TCAA/TCN Subcommittee which aims to fill a policy vacuum in Washington and Ottawa relating to the fundamental protection of citizens’ rights to privacy? This group will formulate consistent guidelines to enable federal and state agencies in the US and Canada to enforce and monitor their laws—will their guidelines affect INCE members? Those who advise or give expert testimony to government agencies, defense/security organizations, courts, and large institutions in financial services, healthcare or education likely find themselves in a rapidly shifting landscape and recognize the need to respond with new research and professional practices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".