Bibliographic record
Abstract
From my own experience working in Institutional Research for the past seven years, there is not a proper, reliable, and comprehensive model for forecasting student enrolment quickly. In many funding formulas, enrolment is the main driver of government grants and student tuition fees, which are sources of income to the university. Existing enrolment management tools developed within Institutional Research departments tend to be “ad hoc” spreadsheets with multiple individuals manipulating them with the result that the output comes too late for departments to take remedial action in terms of their budgets and does not provide multiple scenarios in support of strategic decision-making. The purpose of this study is to describe a functional automated enrolment projection system methodology I developed from scratch through a case study of the Faculty of Arts & Science at the University of Toronto. My primary research was to actually build the model. The model in effect, is the thesis. The system provides multiple scenarios that allow senior management in a multi-campus university system to generate multiple income scenarios, enabling them to make well-informed decisions concerning the operation of their institution and timely calculation and allocation of resources to academic departments. The study then shows how this addresses the problems of “ad hoc” approaches, and how it may be applied in other situations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".