MétaCan
Menu
Retour à la cohorte
Enregistrement W1704534335 · doi:10.18438/b8488s

Econometric Analysis Suggests Possible Crowding Out of Public Libraries by Book Superstores among Middle Income Families in the 1990s

2007· article· en· W1704534335 sur OpenAlexvenueno aff
Stephanie Hall

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2007
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Administration
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMultinomial logistic regressionPopulationCensusHousehold incomeLogistic regressionEconometric modelLogitDemographic economicsCrowdingDemographyGeographyEconomicsStatisticsEconometricsPsychologySociologyMathematics

Résumé

récupéré en direct d'OpenAlex

Objective – To determine the effect of large bookstores (defined as those having 20 or more employees) on household library use. Design – Econometric analysis using cross-sectional data sets. Setting – The United States of America. Subjects – People in over 55,000 households across the U.S.A. Methods – Data from 3 1996 studies were examined using logit and multinomial logit estimation procedures: the National Center for Education Statistics’ National Household Education Survey (NHES) and Public Library Survey (PLS), and the U.S. Census Bureau’s County Business Patterns (CBP). The county level results of the NHES telephone survey were merged with the county level data from the PLS and the CBP. Additionally, data on Internet use at the state level from the Statistical Abstract of the United States were incorporated into the data set. A logit regression model was used to estimate probability of library use based on several independent variables, evaluated at the mean. Main results – In general, Hemmeter found that "with regard to the impact of large bookstores on household library use, large bookstores do not appear to have an effect on overall library use among the general population” (613). While no significant changes in general library use were found among high and low income households where more large bookstores were present, nor in the population taken as a whole, middle income households (between $25,000 and $50,000 in annual income) showed notable declines in library use in these situations. These effects were strongest in the areas of borrowing (200% less likely) and recreational purposes (161%), but were also present in work-related use and job searching. Hemmeter also writes that “poorer households use the library more often for job search purposes. The probability of library use for recreation, work, and consumer information increases as income increases. This effect diminishes as households get richer” (611). Finally, home ownership was also correlated with higher library use. Households with children were more than 20% more likely to use the library (610). Their use of the library for school-related purposes, general borrowing, program activities, and so on was not affected by the presence of book superstores. White families with children were somewhat less likely to use the library, while families with higher earning and education levels were more likely to use the library. Library use also increased with the number of children in the family. Shorter distances to the nearest branch and a higher proportion of AV materials were also predictive of higher library use. Educational level was another important factor, with those having less than high school completion being significantly less likely to use the library than those with higher levels of educational attainment. Conclusions – The notable decline in public library use among middle income households where more large bookstores are present is seen as an important threat to libraries, as it may result in a decline in general support and support for funding among an important voting block. More current data are needed in this area. In addition to the type of information examined in this study, the author recommends the inclusion of information on funding, support for library referenda, and library quality as they relate to the presence of large bookstores.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,090
Score d'incertitude au seuil0,178

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,285
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2007
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueEvidence Based Library and Information PracticeMême sujetLibrary Science and AdministrationTravaux en français237 207