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Enregistrement W225686392

New Decision Tool to Evaluate Award Selection Process. (Applied Research)

2002· article· en· W225686392 sur OpenAlexaboutno aff
Richard Thornley, Matthew W. Spence, Mark Taylor, Jacques Magnan

Notice bibliographique

RevueJournal of Research Administration · 2002
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Sciences Research and Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGovernment (linguistics)Quality (philosophy)Medical educationPolitical scienceMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Introduction Established by the Government of Alberta in 1979, the Alberta Heritage Foundation for Medical Research (AHFMR) supports health research at Alberta universities and other research-related institutions. The foundation supports nearly 230 faculty-level researchers recruited from Alberta and around the world, and approximately 500 researchers-in-training (i.e., summer students, graduate students, and post-doctoral fellows, collectively known as trainees). The AHFMR's gross expenditure for fiscal year (FY) 2000-2001 was approximately $53 million, of which $6.7 million (12.6%) was committed to the funding of trainees. (1) This article describes the foundation's initiative to improve the peer review process for its competitive training awards. Peer review is frequently used for both ex ante and ex post evaluation of the quality of the scientific enterprise (Geisler, 2000; Kostoff, 1992; Luukkonen-Gronow 1987; United States General Accounting Office, 1997). Ex ante evaluation assesses quality in advance of performance, as in the case of applications for research funding. Conversely, ex post evaluation assesses quality retrospectively, as in the case of papers submitted to scientific journals. The case described here entails ex ante review of applications for funding, to anticipate the future performance of research trainees. The AHFMR's original review process for training award applications considered three general criteria: (a) the quality of the candidate, (b) the appropriateness of the proposed research environment, and (c) the merit of the proposed research project. Applications were rated following a multiple-step committee process on a scale of 0 to 5, the single score representing an aggregation of performance in relation to all criteria. Zero is considered an unacceptable application whereas a score of 5 is an outstanding application. This approach was used by the foundation to review applications for its training awards until the end of FY2000, when the foundation piloted the new process described here. Geisler (2000) suggested that peer review should be well-defined, rational, fair, timely, cost-effective, anonymous, and responsive. While most of these general characteristics were reflected in the AHFMR's original review process for its training awards, a number of specific issues provided the incentive for the foundation to try to improve the process. First, the number of proposals submitted was increasing and there was a need to more efficiently evaluate them. In FY1997, the AHFMR received 182 applications for full-time studentships, as compared to 276 in FY2000 and 307 in FY2001. This resulted in the need for more reviewers, most of whom were reporting that they had increasingly less time to devote to such activities. Also, the increase in proposals meant that committees were faced with extending the duration of their meetings or spending less time reviewing each application, neither of which was considered to be a desirable alternative. This issue was complicated by an increase in turnover on the foundation's review committees. In general, this may have been in response to reviewer fatigue, a recent and widespread phenomenon in the research funding sector resulting from a proliferation of requests to individuals to sit on review panels (Brzustowski, 2000a; Brzustowski, 2000b; Cunningham, Boden, Glynn, & Hills, 2001; Smith, 2001). There was a sense that turnover resulted in less consistency in the application of criteria within and between competitions, and an increased administrative burden in recruiting and training committee members. Two trends relating to scores awarded to applications also influenced the AHFMR's decision to redesign its review process. In theory, the overall score awarded to each application represented an integration of all parts of the application; however, in practice each reviewer's interpretation resulted in variable weighting of different criteria. …

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)0,092
score de la tête « metaresearch » (Gemma)0,261
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,908
Score d'incertitude au seuil0,486

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0920,261
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0150,012
Études des sciences et des technologies0,0020,002
Communication savante0,0100,007
Science ouverte0,0030,004
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0370,007

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 Opus0,446
Tête enseignante GPT0,635
Écart entre enseignants0,189 · 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
DomaineIncitatifs
GenreMéthodes

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
Publié2002
Routes d'admission1
Résumé présentoui

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