Collecting and Selecting: A Tale of Training and Mentorship
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Notice bibliographique
Résumé
The shifting landscape of collections development and management, in conjunction with changing staffing models and priorities, has required an evolution of selection responsibilities at the University of Toronto. An administratively complex library system with over 40 libraries and three campuses serving over 88,000 students, significant portions of the University of Toronto Libraries collections were historically built by selectors in the centralized Collection Development Department. Over the past decade, the model has evolved from a single individual selecting for all physical and applied sciences to many selectors, and of engineering and computer science disciplines have finally moved to a fully dispersed model where liaisons in the Engineering & Computer Science Library (ECSL) select for their liaison areas. Historically at the larger U of T Libraries, selection and liaison duties have been separate roles, ostensibly to let selectors and liaisons focus on developing the expertise and experience for their specific role. Over time, staffing levels at ECSL and librarian interest have necessitated a shift to a more distributed model for selection. In this paper, the authors will discuss how selection training has evolved over the years to become a robust program that includes ongoing mentorship and support, a new system-wide Collections Community of Practice initiative, and growing selector empowerment and capacity building in e-resource management and assessment through the resource lifecycle. As none of the current ECSL selectors were hired into their positions with selection duties but have had those duties added as the staffing model and requirements of the ECSL has changed, training and mentorship has become an important step in creating and maintaining the high-quality collections on which the University of Toronto prides itself. The paper will also look at the experience of the ECSL librarians taking on selection for their liaison areas and the benefits and challenges of adding on the extra work and responsibility. The drawbacks and rewards of dispersing selection more generally will be discussed, as well as the mentorship and feedback in terms of collections philosophies as more experienced selectors train and mentor their colleagues new to this role.
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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 | 0,001 |
| 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 | 0,002 |
| Science ouverte | 0,000 | 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 découle