A Forest of Forests: Constructing a centre-usage profile as a source of outcomes assessment
Bibliographic record
Abstract
Most writing-centre administrators collect centre-usage information because it can generate one of the most basic forms of assessment. Such assessment can and often does determine resources in the institutional-funding process. In addition to responding to the call since the 1980s for rigorous scientific assessment issued from researchers and professionals in the field of writing centre research, assessment-based activities have also become necessary for accreditation, budget, and educational-accountability purposes at both institutional and programmatic levels. This paper reports on a usage-profile analysis of an outcomes-assessment project in the context of a newly established language-support unit. The centre-usage profile analysis focused on the 2,932 tutoring sessions conducted during the academic year, which involved 1,100 different users. In addition to the findings’ implications for writing-centre research and practice, the information about the approach used in implementing this component may be useful to administrators, researchers, and practitioners in academic language-support units across institutions of higher education.
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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 | 0.027 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".