Building Indicators by Consensus for Common Attributes
Bibliographic record
Abstract
Abstract – To address the new process of graduate attributes (GAs) assessment as required by the Canadian Engineering Accreditation Board (CEAB), the University of Waterloo (UW) employed six Graduate Attributes Lecturers (GALs) and four Accreditation Assistants (AAs) with a key role of leading the outcomes assessment process in each of the engineering departments. The GALs work collaboratively with each other and their departments to come up with a process of outcomes assessment. The collaboration methods and techniques used by the GALs in developing shared indicators for the common GAs are proving to be highly effective, and have led to significant progress. One of these methods is a structured brainstorming sessions for developing measurable performance indicators for the common GAs.
 The following paper describes in detail the collaboration methods and techniques used by the GALs and AAs to develop shared indicators for the professional skills GAs. The paper also discusses the factors that proved to be successful in the whole process as well as the challenges faced by the team.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".