Notice bibliographique
Résumé
Montreal was a hospitable and popular site for the 2013 Annual Meeting, which was attended by nearly 600 people. In the off-season, downtown Montreal seemed generally dominated by the pleasant bustle of business and students from nearby McGill. All seemed quiet at night, but then there was the noble and magnificent traffic jam after the Canadiens' hockey game Monday night, which we could all view as light sculpture from our perch in the Salon Club at the SIG/III International Reception. The reception was a significant highlight of the meeting as 41 different countries were represented this year. I was especially impressed with the energy and enthusiasm of the newly created Asian Chapter and of the European Chapter, which won the 2013 Chapter-of-the-Year Award. In this issue of the Bulletin, we give you a taste of the Annual Meeting with a look at a variety of conference activities. We begin in Inside ASIS&T with a photo montage of people, places, sessions, parties and other events we enjoyed in Montreal. We then segue into full coverage of the winners of this year's prestigious ASIS&T Annual Awards and a report from the James Cretsos Leadership Award winner Chirag Shah on what ASIS&T means to him both personally and professionally. Continuing the Annual Meeting coverage in our feature section, we begin with reports from pre-conference workshops by SIG/USE, SIG/MET and SIG/SI, in which these active SIGs offered intense programs of papers, speakers, posters, panels, discussion groups and award presentations focused on their own specialties. Steve Hardin reports on the talk by Jorge Garcìa, this year's keynote speaker. We have also included Award of Merit recipient Carol Kuhlthau's acceptance speech as well as viewpoints on information science from ASIS&T Research Award recipient Susan Herring. On his President's Page, Harry Bruce updates us on upcoming actions being taken as a result of the Web Presence Task Force and on steps for expanding membership. And finally, our RDAP Report is by Christopher Eaker of the University of Tennessee Libraries, who discusses resources available for educating researchers, especially graduate students, about data management.
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 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 | 0,005 | 0,013 |
| 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 | 0,001 | 0,006 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».