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

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

Notice bibliographique

RevueEducation next · 2012
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHigher Education Governance and Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésExcellenceCredibilityPublic relationsBest practiceVariable (mathematics)MarketingSociologyPolitical scienceBusinessLawMathematics
DOInon disponible

Résumé

récupéré en direct d'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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,030
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,991
Score d'incertitude au seuil0,050

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,030
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0110,019
Communication savante0,0150,018
Science ouverte0,0020,008
Intégrité de la recherche0,0040,011
Charge utile insuffisante (le modèle a refusé de juger)0,0030,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,089
Tête enseignante GPT0,399
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeThéorique ou conceptuel
DomaineMéthodes
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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