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Record W2508979217 · doi:10.22329/celt.v9i0.4434

The Marketing of Canadian University Rankings: A Misadventure Now 24 Years Old

2016· article· en· W2508979217 on OpenAlexaffvenueabout
Ken Cramer, Stewart Page, Vanessa K. Burrows, Chastine Lamoureux, Sarah Mackay, Victoria Pedri, Rebecca Pschibul

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsRanking (information retrieval)Rank (graph theory)PsychologyValue (mathematics)Interpretation (philosophy)WelfareEmpirical researchSociologyActuarial scienceStatisticsEconomicsPolitical scienceMathematicsLawComputer science

Abstract

fetched live from OpenAlex

Based on analyses of Maclean’s ranking data pertaining to Canadian universities published over the last 24 years, we present a summary of statistical findings of annual ranking exercises, as well as discussion about their current status and the effects upon student welfare. Some illustrative tables are also presented. Using correlational and cluster analyses, for each year, we have found largely nonsignificant, inconsistent, and uninterpretable relations between rank standings of universities and Maclean’s main measures, as well as between rank standings and the many specific indices used to generate these standings. In our opinion, when assessed in terms of their empirical characteristics, the annual data show generally that this system of ranking is highly limited in terms of its practical or academic value to students. Among other difficulties with the interpretation of ranks, we also discuss the possibility that ranking exercises have unintended, though potentially serious, negative consequences for the intellectual and personal welfare of students.

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 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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.295
Teacher spread0.271 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations3
Published2016
Admission routes3
Has abstractyes

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