Analysis of Small Animal Rotating Internship Applicants’ Personal Statements
Bibliographic record
Abstract
The primary purpose of this study was to identify themes that are consistent across veterinary internship applicants' personal statements and that are correlated with the statements' perceived overall quality. A secondary purpose was to investigate the reliability in personal statement quality scoring among six experienced internship candidate evaluators. One hundred applications to the University of Georgia Small Animal Rotating Internship program were evaluated. Each evaluator wrote a description of what he or she values in personal statements and his or her beliefs about content and presentation in high- and low-quality statements. After statement de-identification, each evaluator reviewed 15 randomly selected personal statements from internship applicants and assigned each a score ranging from 1 to 4 according to the following criteria: 1 = would not rank for an internship; 2 = would rank in the bottom third; 3 = would rank in the middle third; and 4 = would rank in the top third. A subset of these scored personal statements was chosen for qualitative analysis. A qualitative document analysis using grounded theory was performed for both the evaluators' descriptions of preferences in personal statements and the subset of personal statements. Agreement among evaluators' assigned scores was slight (Fleiss's κ = 0.11). Analysis of the evaluator statements and the scored candidate statements indicated that important factors in a personal statement include the applicant's ability to articulate experiences, to convey maturity, to demonstrate understanding of what an internship entails, and to describe reasons for pursuing an internship.
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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".