University Journal Subscription Costs in the UK: An Exploratory Study
Notice bibliographique
Résumé
This poster project focuses on using visual analytics to understand complex data. For this project, we investigated a unique new dataset, “Journal subscription costs - FOIs to UK universities,” published by Stuart Lawson and Ben Meghreblian. They collected data by sending Freedom of Information (FOI) requests to universities in the UK for information on the subscription costs to seven major publishers over a five year period, using the website whatdotheyknow.com. This work is ongoing, with data added as universities respond. The dataset was also chosen due to its CC-BY license, which allowed us the flexibility to remix and build upon the original work. We also supplemented this dataset with publically available UK enrolment data. Database and journal subscription costs are becoming an increasingly hot issue for academic libraries. Many libraries sign non-disclosure agreements with publishers, which limits their ability to leverage or bargain over subscription prices. Although it is often reported that subscription costs have been on the rise and that libraries are under increasing financial pressure, it is unclear exactly how 2 much libraries are paying and to whom. What is clear is that these increasing subscription prices have caused many to question the traditional publishing model, and whether the added value from traditional publishing is worth the cost. A recent Nature news feature, for example, discussed the benefits of an Open Access journals model that is able to operate profitably and more efficiently while free access to articles for the public. Other recent research by Bergstrom et al. has also used FOI requests to find information about journal subscription prices in the United States. When comparing their data with factors such as total university enrolment, number of PhD students, and whether the school was a Research 1 or Research 2 institution, they concluded: The contracts that we have seen show remarkable institution specific price variations that cannot be explained by university characteristics such as enrolment and PhD production. Some institutions have been quite successful in bargaining for lower prices, whereas others may not have been aware that better bargains can be reached. Perhaps this variation explains publishers’ desire to keep contract terms confidential (Bergstrom, Courant, McAfee, & Williams, 2014). By using a similar dataset, and merging the data with openly available UK university enrolment and geolocation data, this project builds upon research by Bergstrom et al. and, in the process, contributes to a new body of research creating transparency in the library purchasing field. This research is preliminary in nature. At the time of data cleaning and analysis, the datasets were still incomplete. However, we investigate the following questions: • How have subscription costs generally changed over the 5-year period from 2010-2014? • How does university enrolment compare with the amount that universities pay major publishers? • Does the geospatial location of a university play a role in how much they pay in subscription costs to major publishers? As the data is not very granular, it is difficult to answer any of these questions with certainty; however, we discovered several patterns that warrant further investigation. Why, for example, does it appear that institutions with average enrolment size pay the most for journal subscriptions, while those with the lowest and highest enrolment appear to pay less? Why are there so many drastic changes in price year to year at many universities? Future research should focus on acquiring more granular data and investigating these inconsistencies.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| 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 ».