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Record W350228331

Best Practices Are the Worst: Picking the Anecdotes You Want to Believe

2012· article· en· W350228331 on OpenAlexaboutno aff
Jay P. Greene

Bibliographic record

VenueEducation next · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsExcellenceCredibilityPublic relationsBest practiceVariable (mathematics)MarketingSociologyPolitical scienceBusinessLawMathematics
DOInot available

Abstract

fetched live from OpenAlex

Surpassing Shanghai: An Agenda for American Education Built on the World's Leading Systems Edited by Marc Tucker Harvard Education Press, 2011, $49.99; 288 pages. Best is the worst practice. The idea that we should examine successful organizations and then imitate what they do if we also want to be successful is something that first took hold in the business world but has now unfortunately spread to the field of education. If imitation were the path to excellence, art museums would be filled with paint-by-number works. The fundamental flaw of a approach, as any student in a half-decent research-design course would know, is that it suffers from what is called selection on the dependent variable. If you only look at successful organizations, then you have no variation in the dependent variable: they all have good outcomes. When you look at the things that successful organizations are doing, you have no idea whether each one of those things caused the good outcomes, had no effect on success, or was actually an impediment that held organizations back from being even more successful. An appropriate research design would have variation in the dependent variable; some have good outcomes and some have bad ones. To identify factors that contribute to good outcomes, you would, at a minimum, want to see those factors more likely to be present where there was success and less so where there was not. Best lacks scientific credibility, but it has been a proven path to fame and fortune for pop-management gurus like Tom Peters, with In Search of Excellence, and Jim Collins, with Good to Great. The fact that many of the companies they featured subsequently went belly-up--like Atari and Wang Computers, lauded by Peters, and Circuit City and Fannie Mae, by Collins--has done nothing to impede their high-fee lecture tours. Sometimes people just want to hear a confident person with shiny teeth tell them appealing stories about the secrets to success. With Surpassing Shanghai, Marc Tucker hopes to join the ranks of the gurus. He, along with a few of his colleagues at the National Center on Education and the Economy, has examined the education systems in some other countries with successful outcomes so that the U.S. can become similarly successful. Tucker coauthors the chapter on Japan, as well as an introductory and two concluding chapters. Tucker's collaborators write chapters featuring Shanghai, Finland, Singapore, and Canada. Their approach to greatness in American education, as Linda Darling-Hammond phrases it in the foreword, is to ensure that our strategies must emulate the best of what has been accomplished in public education both from here and abroad. But how do we know what those best practices are? The chapters on high-achieving countries describe some of what those countries are doing, but the characteristics they feature may have nothing to do with success or may even be a hindrance to greater success. Since the authors must pick and choose what characteristics they highlight, it is also quite possible that countries have successful education systems because of factors not mentioned at all. Since there is no scientific method to identifying the critical features of success in the best-practices approach, we simply have to trust the authority of the authors that they have correctly identified the relevant factors and have properly perceived the causal relationships. But Surpassing Shanghai is even worse than the typical best-practices work, because Tucker's concluding chapters, in which he summarizes the common best practices and draws policy recommendations, have almost no connection to the preceding chapters on each country. That is, the case studies of Shanghai, Finland, Japan, Singapore, and Canada attempt to identify the secrets to success in each country, a dubious-enough enterprise, and then Tucker promptly ignores all of the other chapters when making his general recommendations. …

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 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)0.009
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.991
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0110.019
Scholarly communication0.0150.018
Open science0.0020.008
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0030.002

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 head0.089
GPT teacher head0.399
Teacher spread0.310 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations0
Published2012
Admission routes1
Has abstractyes

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