Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
FACTS.That might seem self-evident.However, when the decisions are for a very large and complex institution, or when the issue at hand is complicated or difficult to measure, facts can be hard to come by.In many instances, decisionmakers are so used to being unable to get much useful data that the standard operating procedure is to proceed on the basis of experience.My career has been primarily in the business world, where the advent of the computer has made data dramatically more usable and accessible than ever before.I've worked to build systems that collect and translate this data into information ready for analysis.And I've seen the dramatic effect it can have on focusing efforts, making improvements and measuring results.When I began working with the Chicago Pubic Schools, one of my contributions was to apply my experience in transforming and managing large organizations through the use of information.CPS had warehouses full of data.Unfortunately, in warehouses it was inaccessible and therefore almost useless.I think one of my most significant accomplishments at CPS was building a technology infrastructure that allows for ready access to and the use of data.Melissa Roderick, SSA's Hermon Dunlap Smith Professor, has been at the forefront of showing how the information that's now available at CPS can help us make better decisions about public education in Chicago.Her study of high school dropouts as the co-director at the Consortium on Chicago School Research, for example, found that statistically, if a student finished freshman year, they were much more likely to graduate high school.Those findings had a significant impact on CPS strategies, from the introduction of the Freshman Connection program to intense efforts to guide students through that first critical freshman year.This approach to research-to find the real story in the data, to test assumptions, to use information to create evidence-based practice-is part of the strength and innovation at SSA. Faculty at the School are combing through data to find new, better information about everything from stopping gun violence to building better substance abuse treatment programs."Bottom line" can sound like a harsh term when it comes to social services and social justice-but it can mean more than just judging fiscal costs.It can also mean a thorough, scientific look at conditions and results in the real world.In the very best sense of the term, SSA gets to the bottom line of the policies and practices in place to solve society's toughest, most important issues.I am consistently impressed with the work going on at the School to use data-facts-to help shape the effectiveness of policies that improve our society.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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.
score_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