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Record W227650991

Those Magazine Rankings: Let's Beg Them To Stop.

2000· article· en· W227650991 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProfessional School Counseling · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLiberal arts educationPsychologyOrder (exchange)Affect (linguistics)Higher educationRank (graph theory)Public relationsMathematics educationMedical educationSociologyPolitical scienceBusinessLawMathematicsMedicine
DOInot available

Abstract

fetched live from OpenAlex

During the college choice process, students and their families need to research and evaluate colleges in order to find colleges where the student can maximize his or her chances for success. It is important for students and parents to understand their own needs and wants, understand the unique characteristics of some colleges, and make a list of colleges that would be good matches for the student. The emphasis should be on encouraging the student and family to consider the when researching colleges. If done well, the students will learn something about themselves and about the many educational options that are available. Counselors can tell when a student is using the college rankings in U.S.News & World Report. I hear comments such as: My parents only want me to look at tier 1 colleges; uncle said, `don't go there, that's a tier 2 college'; and is the top-ranked liberal arts college in the country? In the search for good information, many students turn to the rankings without fully understanding them. Good college help students find a good fit between the student and the college. Resources that rank colleges encourage students to look more at the label and not the fit. While many college admissions professionals strongly believe that students should not use the college rankings that appear in national magazines and books, the rankings continue to influence decisions and affect the college admissions scene (McDonough, Antonio, Walpole, & Perez, 1997). These rankings misrepresent the colleges (Hoover, 1996) and steer students toward making decisions for the wrong reasons, such as choosing a college solely because of its ranking and not the match for the student (McGuire, 1995). Many prominent educational leaders such as Gerhard Casper, the President of Stanford University, have criticized the rankings and their role in the college choice process (McKinley, 1996; Ray, 1997). Around the same time as these initial criticisms, school counselors received a letter from the Editor, Special Projects of U.S.News & World Report stating that the magazine had made an in its rankings which impacted the ranking of American University (M. Elfin, personal communication, October, 31, 1996). This error highlighted one of the many problems with the rankings. The problems with the rankings continued when in March 1997, The Best Graduate Schools misranked 44 law schools (Fallows, 1997). Fundamental Flaws In my view, the following are fundamental flaws in the magazine's rankings: * They attempt to quantify the unquantifiable (e.g., subjective phenomena). * They confuse selectivity with quality (i.e., low acceptance rates, high yield, and high SAT scores increase a college's ranking). To quote from Colleges that Change Lives (Pope, 1996): A Canadian once observed that the American practice of judging colleges by the academic records of the high school seniors they pick is like judging the quality of hospitals by the condition of the patients they admit. What happens during the stay is what counts (p. 2). * They use a formula that is arbitrary, subjective, and changes year-to-year (e.g., U.S.News & World Report admits (Wildavsky, 1999, p. 1] that it was a change in the rankings methodology that helped move Caltech to the number one spot this year). * They mix and, as a result, obscure helpful statistics (e.g., a statistic such as retention rate is important, but gets lost in the formula). * They use artificially aggregated variables (e.g., resources is a combination of student /faculty ratio, percent of professors with terminal degrees, percent of full-time faculty, average faculty salary, and average class size) and arbitrarily weighted variables (e.g., alumni giving is used as a proxy for alumni satisfaction and is 5% of the total score in the formula). * They assume that students will view the rankings critically and objectively when they may not have the emotional or intellectual maturity to do so. …

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.009

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.

Opus teacher head0.039
GPT teacher head0.406
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it