Cool Colleges: For the Hyper-Intelligent, Self-Directed, Late Blooming, and Just Plain Different
Bibliographic record
Abstract
Cool Colleges: For the Hyper-Intelligent, Self-Directed, Late Blooming, and just Plain Different Donald Asher Ten Speed Press, 2000, 224 pp., $14.95 (soft cover) Ever wish you could find an unconventional guide to colleges and universities? One that is thought-provoking and loaded with interesting but little known tidbits about higher education? Cool Colleges may be the book for you. Written by a business writer and speaker who has spent a decade visiting colleges across the U.S. and Canada and who has children of his own who are contemplating the college scene, this book explores the diversity of opportunity that is available in higher education today. Using graphics and page design to package information in a readable and digestible manner, a reader can open this at any page and find useful facts, observations, and appraisal. But a word of caution is in order. To obtain maximum usefulness and understanding of what is being presented, the reader should first consult the Introduction. It outlines what the book purports to do and its limitations and omissions as well. The author explains his research methods and biases and offers his opinion that a undergraduate experience can often best be obtained at a smaller, perhaps lesser-known, four-year college rather than at a large university. As he states, This guide is not meant to be the primary guide in a counselor's office, nor the only guide a student should consider. One of my explicit goals is to provide information not readily available everywhere. The cost of going to college, a major consideration for many students, is not discussed here. The author believes prospective enrollees should seek a good college fit, then consider the financial aid package offered and make a decision based on hard data rather than trust the claims of a campus viewbook. In discussing a college the author wishes to highlight, he provides a summary of its characteristics of particular interest to undergraduates, its pluses and minuses as an institution, and a cross apps feature. The latter provides a handy means to find other, similar schools if exploration is part of the adventure in choosing a college. He cautions the reader to be sure to get the right name of the college being considered and cites several examples of schools with similar names but having very different profiles. For example, nineteen schools have Washington as a part of their names. Wheaton College in Massachusetts and Wheaton College in Illinois are two fine liberal arts but distinctly different. Schools described by the author as innovative, those operating on the block plan, co-ops, the Public Ivys, engineering schools, military academies, schools known as work colleges, those with a religious affiliation, those with a tribal affiliation, schools for auctioneering, modern railroading, and comedy are all included. …
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.204 | 0.151 |
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".