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Record W1537197831

An Impossible Dream? The Efficacy of Using Rankings to Improve the Perception of a Non-OECD Country's Educational System.

2008· article· en· W1537197831 on OpenAlexaboutno aff
Chris J. Foley

Bibliographic record

VenueCollege and university · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationRanking (information retrieval)Context (archaeology)Quality (philosophy)Political scienceRank (graph theory)Value (mathematics)PerceptionMarketingEconomic growthEconomicsBusinessPsychologyGeographyStatistics
DOInot available

Abstract

fetched live from OpenAlex

Rankings have an increasing impact on higher education. Regardless of their true ability to judge a university's success or failure, rankings are used by students, their families, and, increasingly, policy makers to define the quality of institutions. Rankings have gone beyond comparisons among universities within individual countries: Today, they compare universities across geographic regions, and a few rank universities in a global context. This paper reviews the impact of rankings on uni- versities and explores the methodology behind some of the more popular global ranking systems. It then discusses the impact these rankings could have on perceptions of the educa- tion systems of countries not included in the Organi- zation for Economic Coop- eration and Development (OECD) (the assumption being that OECD countries tend to have the longest history of utilizing rankings and of marketing their education systems internally and externally) and explores whether these rankings equally influence the perceptions of OECD and nonOECD educational systems as well as the feasibility of improving the rankings of institutions from non-OECD countries. As a case study, this paper focuses on Chile and discusses the relative value and influence of global rankings in regard to the country's higher education system. THE IMPORTANCE OF RANKINGS TO INSTITUTIONAL IMAGE To the general public and to many policy makers, rankings (or league tables, as they are referred to in the United Kingdom) are synonymous with quality. They are a short-hand method used to assess whether one university is better than another (Sarraf et al. 2005). However, what rankings actually measure is often of a secondary nature to information consumers. Although some semblance of rankings existed in the United States prior to the 20th century, university rankings in a mass fashion began in 1983 with the publication of rankings of U.S. universities in U.S. News & World Report. Since that time, country- or region-specific rankings have been developed in the United Kingdom (The Times, The Guardian), Canada (Maclean's), Asia (Asiaweek), Europe (The Times), China (netbig, Guangdong Institute of Management Science, Research Centre for China Science Evaluation of Wuhan University, The Chinese Universities Alumni Association, the Shanghai Institute of Educational Science), Japan (Asabi Shimbun, Diamond, Kawai-juku, Recruit Ltd.), Germany (che /Stern), Poland (Perspektywy), and Australia (Melbourne Institute, Good Guides) (Liu and Liu 2005; Van Dyke 2005; Yonezawa, Nakatsui and Kobayashi 2002). In addition to rankings developed by magazine publishers, some governments have developed methodologies by which to compare institutions (e.g., Russia, China, and Kazakhstan). The value and impact of rankings vary by constituency and country. Despite dispute over the validity of their methodology (Bowden 2000; Eccles 2002; Sarraf et al. 2005; Turner 2005; Van Dyke 2005), rankings remain popular, and the number of rankings available to the public grows. Though many rankings are constructed with the intention of influencing student choice, their actual influence on students is unclear at best. Their impact on student choice in the United States appears to be minimal (Kinzie et al. 2004), and in the United Kingdom (Eccles 2002) and in Canada, their impact is either puzzling (in the case of larger institutions) or localized to smaller, primarily undergraduate universities (Drewes and Michael 2006). Though it would be inappropriate to extrapolate these findings to all rankings in all contexts, a developing body of evidence indicates that rankings do not have a significant influence on college choice. Nevertheless, rankings in the United States do have an impact on university administrators (Hossler 2001a, 2001b; Kinzie et al. 2004). In the quest to improve the reputations of their institutions, administrators pursue policies intended to improve their institutions' ranking. …

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.140
metaresearch head score (Gemma)0.245
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.140
Threshold uncertainty score0.738

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1400.245
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.012
Scholarly communication0.0100.015
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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