Benefits of Unionization Still Unclear for U.S. Academic Libraries and Librarians
Notice bibliographique
Résumé
A Review of: Applegate, R. (2009). Who benefits? Unionization and academic libraries and librarians. The Library Quarterly, 79(4), 443-463. Abstract Objective – To investigate the quantitative benefits of unionization for libraries, librarians, and students at academic libraries in the United States. Design – Quantitative analysis of existing datasets. Setting – Academic libraries in the United States. Subjects – One thousand nine hundred four accredited colleges and universities in the United States. Methods – Institutions that provided data for both the National Center for Education Statistics (NCES) Academic Libraries Survey (ALS) and the NCES Integrated Postsecondary Education Data Service (IPEDS) survey series in 2004 were considered for inclusion in this study. Of these institutions, only those with student populations over 500 and employing more than one librarian were included. The study did not include specialized libraries at institutions where “most of their degrees were awarded in a single area” (p. 449). The institutions were categorized by type derived from data by Carnegie and the Association of Research Libraries. The final categories were: ARL, Doctoral Non-ARL, Masters, Baccalaureate, and Associates. Governance was determined by using information from IPEDS that classified the institutions as public, private not-for-profit, and private for-profit. Unionization status was derived from the Directory of Faculty Contracts and Bargaining Agents in Institutions of Higher Education. After private not-for-profit and private for-profit classifications were collapsed into one category, governance and unionization information were combined to create the final governance categories of: private, public nonunionized, and public unionized. The study analyzed the following characteristics in terms of institution type, governance, and institution type and governance interaction: ratio of students to librarians, ratio of library expenditures to institutional budget expenditures, average librarian salary, percentage of staff who were librarians, librarian salaries as a percentage of staff salaries, and percentage of the library budget spent on staff salaries. Main Results – Analysis revealed statistically significant differences (p< .05) between governance and student-librarian ratio and between governance and percentage of library budget spent on staff salaries. No consistently beneficial relationship between governance and student-librarian ratio was determined. A consistently positive relationship was found between governance and percent of the library budget spent on librarian salaries; all public unionized institution types had higher percentages of the library budget devoted to librarian salaries than private and public nonunionized institutions. All five dependent variables showed statistically significant differences (p< .05) when analyzed by institution type. Analysis by institution type and governance interaction found statistically significant differences (p< .05) for student-librarian ratios, librarian salary, and percentage of library budget devoted to staff salaries. Strong R2 values were determined for the dependent variables of: staff salaries as a proportion of library budget (.51) and student-librarian ratio (.34). Conclusion – Based on the results, the author stated that unionization appears to have positive or neutral benefits for the library, librarians, and students, regardless of institutional type. Further quantitative and qualitative research is needed to analyze the effects of unionization on library quality.
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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 | 0,015 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,014 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,013 | 0,017 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,003 |
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 ».