OBACIS Phase III: Accreditation and Grading Sheets (AGSs) — The Excel-App
Bibliographic record
Abstract
Abstract – In this paper, an Excel Add-in for automating grade recording and graduate attributes assessment at the course level is presented. Course learning outcomes, accreditation units (AU) input parameters and some other course-specific related data are documented as well. A set of student performance reports are generated and are utilized for closing the loop of the continuous improvement activities mandated by the new CEAB accreditation process. The add-in or the Xl-App is one of the three major constituents of the OBACIS framework. The other two are the Windows application or the Win-App; for accreditation administration operations and the web tool or the Web-App; for data compilation reporting process. The Win-App parse the data collected by the add-in (presumably collected via the Web-App as xlsx files or XML files) and integrate them with other program and faculty-level performance assessment and continuous improvement activities. In addition to the role for which it was created, the xl-App can emit the data collected to suit the learning management systems grade books and web marking systems.
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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.033 |
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".