Bibliographic record
Abstract
OBACIS is an integrated framework being developed to accelerate the accreditation reporting work-flow, cut down the reporting cost by an order of magnitude, and close the data-driven continuous improvement loop. This paper focuses on creating a centralized database for compiling accreditation data required for accreditation reporting from various resources such as previous visit accreditation reports, academic calendars, course schedules, and a handful of other resources are used to create what we call the Catalogs. Despite the fact that the Catalogs framework has been developed to meet the reporting standards of Canadian Engineering and Accreditation Board (CEAB), The system can be easily adapted to meet other standards such as ABET and EUR-ACE. The Catalogs are supposed to save a sheer amount of time needed for accreditation reporting and should act as an instrumental tool for accelerating accreditation data collection, creating insightful analyses, and identifying gaps for continuous improvement initiatives at both program and faculty levels.
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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.038 |
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".