Marketing and Assessment in Academic Libraries: A Marriage of Convenience or True Love?
Notice bibliographique
Résumé
Objective – This paper describes the process of cooperation between the Marketing and Assessment Teams at the University of Haifa in Israel, from initial apprehension about working together to the successful marketing of a suite of user studies.
 
 Methods – The first step was a formal meeting in which the leader of the assessment team explained the aims of assessment. For each assessment activity, the assessment team submitted a formal request for assistance to the marketing team, conducted team meetings on how to market each assessment, and met with the marketing team to explain the survey and receive their input on how it should be marketed. Over a 3-year period, 5 joint activities were undertaken: a 1-day, in-library use survey; a wayfinding study, in which 10 new students were filmed as they searched for 3 items in the library; 5 focus group sessions regarding upcoming library renovations; a LibQUAL+® survey measuring perceptions of service quality among the entire campus population; and an online survey of non-users of the library. The success of the assessment/marketing projects was measured by the response rates, the representativeness of the results, and the number of free-text comments with rectifiable issues.
 
 Results – Although the response rates were not very high in any of the surveys, they were very representative of the university population. With over 40% or respondents filling in free-text comments, the information received was used and applied in making service changes, including the creation and marketing of additional group study rooms, improved signage, and the launch of a “quiet” campaign. In addition, a “You said – We did” document was compiled that outlines all of the changes that were implemented since the first four surveys were conducted; this document was published on the library’s blog, Facebook page, and website.
 
 Conclusion – The number of issues that appear in the first “You said – We did” document is a testament to the close and ongoing collaboration between the two teams, from the planning stages of each survey until publication of results and notification of the changes that were implemented.
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 | 0,002 | 0,004 |
| 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 | 0,731 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».