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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.028 | 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".