Bibliographic record
Abstract
Cape Breton University’s (CBU) Community Studies Department works collaboratively to design, implement, and evaluate the core courses of the Bachelor of Arts Community Studies (BACS) degree program. These core courses are called Community Studies (COMS). Unlike other departments, the COMS department is not formed around one particular discipline. The faculty backgrounds are from disciplines such as sociology, adult education, women’s studies, kinesiology, and social work. This mixture of backgrounds, along with the ability to work collaboratively, has been a source of strength for the department. The COMS courses involve process-based learning. Some of the components of these courses are group work, problem solving, critical thinking, reflective learning, self-directed learning, and experiential learning. The role of the faculty is to facilitate the learning objectives of these core courses for the students rather than lecturing on specific topics. Consultation, collaboration and the sharing of materials and ideas among the faculty are vital to teaching in this non-traditional environment. Reviewing teaching methods, course outlines and objectives as well as graduate outcomes are ongoing practices. The students in these courses also need to collaborate. They work in groups to carry out their research and other activities. This paper highlights a model of faculty collaboration and student collaboration in the COMS 300 Community Intervention courses. Students in these process-based learning courses are expected to conduct research and implement projects that address community issues.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".