Overview of the Organization of Scientific Area Committees
Bibliographic record
Abstract
The National Institute of Standards and Technologies (NIST) has transformed the majority of the Scientific Working Groups (SWGs) into the Organization of Scientific Area Committees (OSAC). The OSAC has been created to foster the development of standards and guidelines for the practice of documenting evidence by methods that are technically sound and accepted by the consensus of forensic practitioners. The OSAC is composed of 33 committees arranged in a hierarchy. Potential standards begin in a Subcommittee and must work their way through approval by the Subcommittee, the Subcommittee's parent Scientific Area Committee (SAC), and finally the Forensic Science Standards Board (FSSB) before being adopted into the FSSB Registry of Approved Standards. NIST hopes to find existing standards with technical merit that were developed through a standards development process for adoption as national standards of practice. The OSAC provides forensic scientists the unprecedented opportunity of validating their own scientific practices within the framework of sound scientific practice.
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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.025 |
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".