Lawrence University Hits New Student Enrollment Goal for Fall 2013
Bibliographic record
Abstract
While many colleges around the country are still accepting applications for the 2013-14 academic year two weeks after the traditional May 1 deadline according to a survey by the National Association for College Admission Counseling (NACAC), Lawrence University has met its new student goal for the coming year. According to Ken Anselment, dean of admissions and financial aid, Lawrence has met its enrollment goal of 400 new freshmen for the coming year. He expects to welcome 25 transfer students along with them. “It is both a delight and a relief to say ‘We have a class,’” said Anselment. “Given how long students waited to make their decisions this year, I — along with many of my colleagues at other colleges — were a little anxious in those days heading up to the May 1 deadline waiting to see how they would come together. This group of new students will add to the vitality of the student population we already enjoy.” With this class, Lawrence expects to open the 2013-14 academic year with a full-time, degree-seeking student population of 1,500 students. The incoming class of 2017 represents 32 states and 24 countries. Wisconsin again accounted for the largest percentage of freshmen with just under a quarter of the class, followed by Illinois, Minnesota, California and New York. The new students include Lawrence’s largest class of international students in the past 10 years — 53 — with China accounting for one-third (18) of them. With four each, Jordan and South Korea are sending Lawrence the second-most international students. Twenty percent of the freshmen are domestic students of color, among the highest percentages in school history. Academically, the new class is like the community it joins, in that the plurality ranked in the top 10 percent of their graduating class — more than a quarter of them in the top 5 percent. The average grade point average among the freshmen is 3.61 with an average ACT score of 29. “While we’re always excited to talk about what our students look like before they arrive,” said Anselment, “the thing I always look forward to is seeing how they transform once they’re here.” Nationally, more than 200 colleges were still accepting applications for Fall 2013 freshman and/or transfer admission as of May 16 according to the survey. Seventy-two percent of the respondents to the survey were private institutions and 28 percent were public colleges and universities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.162 | 0.145 |
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".