2013 Fiske Guide Cites Lawrence’s University’s “Eclectic Approach to Learning”
Bibliographic record
Abstract
“A school that can appeal to both the left and right side of students’ brains.” That’s how former New York Times education editor Edward Fiske describes Lawrence University in his just-published “Fiske Guide to Colleges 2013.” The guide, a selective and systematic look at 319 colleges and universities in the United States, Canada and Great Britain, is published annually as a resource for college-bound students and their families on which to base their educational choices. In a profile of Lawrence, Fiske cites the college for its “eclectic approach to learning that attracts interested and interesting students from around the world.” The guide also highlights Lawrence’s commitment to individualized learning, the expertise of the faculty and its broad, off-campus study opportunities. Among the guides features: • Updated overviews of the best and most interesting colleges and universities with details on academics, campus setting, student body, financial aid, housing, food, social life and extracurricular activities • A listing of schools that no longer require the SAT or ACT of all applicants, of which Lawrence is one. • An “If You Apply To” feature, which contains vital information about each college’s admission policies, including deadlines and essay topics. Fiske spent 17 years as education editor of the New York Times. He compiles his guide as a tool to broaden students’ horizons about American higher education and help them select the right college that coincides with their particular needs, goals, interests, talents and personalities.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.031 |
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".