Admissions File Review
Bibliographic record
Abstract
PURPOSE: Although multiple independent sampling (MIS) has been adapted for admissions interviews, its application for assessing written materials in the admissions file has been limited. Currently, admissions file review at the University of Toronto medical school involves one rater per file to enable holistic assessment, which may introduce a halo effect-that is, impressions of one component influencing the evaluation of other components. The authors examined whether MIS file review, through which multiple raters evaluate specific file components independently, may reduce this effect. METHOD: The authors selected a stratified random sample of 300 applicant files from the 2010-2011 admissions cycle for rescoring by MIS. They divided each of the 300 applicant files into their four components (academic transcript, autobiographical sketch, personal statement, reference letters) and rebundled them into packages of 38 same-component items (purposely creating some overlap among packages to assess inter-rater reliability). The authors distributed each package to 1 of 36 raters; thus, each rater evaluated only one of four components across many applicants. The authors compared the inter-component reliability and factor analysis of MIS with that of holistic scoring. RESULTS: Ratings were returned for all applicants. Inter-component reliability (Cronbach alpha) was 0.69 for holistic scoring and 0.29 for MIS. Factor analysis showed all components loading heavily onto one factor in the holistic approach and onto three factors in the MIS method. CONCLUSIONS: Using MIS to assess the admissions file may reduce the halo effect and should be considered when evaluating applicants' written submissions.
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 | 0.001 | 0.037 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.463 | 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; both teacher heads agree on what is shown here.
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".