Cost-Benefit Analysis of Security Gates and Collection Shrink in the Academic Library
Notice bibliographique
Résumé
Objective – Two academic libraries serving public universities in the United States faced a similar choice of whether to keep magnetic security gates in place and pay for their upkeep. To make informed decisions, researchers at Chicago State University (CSU) and Western Kentucky University Libraries (WKUL) ran concurrent studies with different models of cost-benefit analysis to determine whether magnetic security gates were worth the expense. Security gates were physically present but not functional at both institutions during the study. Exploring different methods of analysis provided opportunities to discuss whether security gates are effective at preventing collection shrink, identify issues in measuring the costs of theft, and explain why WKUL chose to remove magnetic security gates altogether. Methods – At CSU, we measured loss over a six-month period on a sample set of 110 monographs. The cost of replacing missing books, including labor and incidentals, was used to approximate the cost of shrink in an equivalent percentage of materials from the main collection housed in open stacks. We compared the expected cost of replacing the security gates to the estimated cost of shrink to determine how much loss security gates would need to prevent to justify the cost of maintaining security gates. While the sample was neither randomized nor large enough to draw conclusions, trialing this model of cost comparison presented an opportunity for discussion. WKUL had a practice of running a near continuous inventory prior to this study. In 2024, staff inventoried the entire collection held in open stacks. This provided a precise number of how many items went missing during that timeframe. We compared the number of missing items to the quoted cost of annual service and maintenance fees to determine whether maintaining security gates would justify the cost. Simply dividing the annual service fees by the number of missing items provided a dollar value per missing item that security gates would have had to save in order to justify their expense. Results – The calculated annual cost of collection shrink at CSU is $136,335, much more than the estimated $85,121 to replace the magnetic security gates. Inferring a similar rate of shrink to the sample set, despite the problems with the method, suggests that new security gates would have to prevent 62.44% of total loss to pay for themselves in the first year, 33.66% in two years, and 24.07% over three years. While we did not draw firm conclusions from this trial analysis, it is evident that security gates would likely save money over the span of a few years. WKUL found that 99 individual items went missing from all collections housed in open stacks over 2024. The quoted annual subscription fee for the four sets of security gates at WKUL is $8,894. These data suggest that security gates at WKUL must prevent an average of $89.83 in lost value per missing item to justify the annual fees alone. Another way of describing this is that if each item that went missing cost $89.83, security gates would have to stop 100% of collection shrink to make up for their annual subscription fees. A more likely scenario is that security gates would have prevented 50% of the collection shrink, and materials would have had to carry an average value of $179.66 for security gates to pay for themselves. Conclusion – At first glance, the data from CSU suggests that magnetic security gates have the potential to prevent enough collection loss that they pay for themselves. The library at CSU has an annual operating budget of just over $2 million, and an annual loss of nearly $140,000 in value would be unsustainable. However, the data from WKUL suggest it would be difficult to justify the annual subscription fees, let alone the cost of replacing defunct hardware. Further inquiry and discussion are needed to explore variables not covered in this study, including employee theft, security gate efficacy, the lifecycle of library materials, and how security gates may affect students’ feelings of belonging and inclusion.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 source (Gemma direct ou Codex distillé), 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 ».