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Enregistrement W2129282682 · doi:10.1002/meet.1450420175

Information grounds and everyday life

2005· article· en· W2129282682 sur OpenAlexaff
Karen Fisher, Lynne McKechnie, Tom Dobrowolsky, Betty Marcoux, Charles M. Naumer, Carolyn Burrell

Notice bibliographique

RevueProceedings of the American Society for Information Science and Technology · 2005
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Administration
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Everyday lifeInformation literacyEthnographySociologyInformation sharingPsychologyInterviewInformation flowPublic relationsSocial psychologyPolitical scienceComputer scienceGeographyPedagogyWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Information grounds can occur anywhere and are based on the presence of individuals. People gather at them for a primary, instrumental purpose other than information sharing. Information grounds are attended by different social types, most—if not all of whom—play expected and important, albeit different, roles in information flow. Social interaction is a primary activity such that information flow is a byproduct. People engage in formal and informal information sharing, and information flows in many directions. People use information obtained at information grounds in alternative ways, and benefit along physical, social, affective and cognitive dimensions. Many sub-contexts exist within an information ground and are based on people's perspectives and physical factors; together these sub-contexts form a grand context. Information Grounds during Baby Story Time Ethnographic observation and interviewing was conducted at eleven, 30 minute sessions of public library baby/adult caregiver storytimes to discover what actually happens at these sessions. One of the surprising results was that the adult caregivers engage in everyday life information sharing about topics such as parenting, health, child development, travel, and daycare. These findings suggest that storytime programs, in addition to being important literacy events for the children participating, also act as information grounds or informal sites where information is shared. This study was funded by ALA's Carroll Preston Baber Research Grant award. Seattle contains a small, though active, Polish-American community. Transcending any one neighborhood, this network comprises several social hubs, events, and gathering places in order to actively promote Polish culture and to connect Polish-Americans with their ethnic roots. In this ethnographic study, 15 members were interviewed and participant observation was conducted in three popular gathering places. Observations involved detailed descriptions of the events taking place, the places themselves, the people and groups present, and the social phenomena which occurred in them. Findings revealed the participants' information grounds and how they were used to disseminate everyday information. Additionally, findings addressed how the social nature of information exchange, via information grounds, functioned to establish a sense of community and to maintain Polish ethnic identity within this geographically disparate social network. A world agricultural capital, Yakima Valley in Washington State attracts many migrant Hispanic farm workers. The combined transitory nature of their work along with low wages, education, healthcare and other factors contributes to their label as information poor: meaning they have little success in meeting substantial needs for information. Our study of farm workers and their families comprised interviews and observation with 60 individuals at community technology centers. The most popular information grounds were church, school and the workplace; other sites included the farm workers' medical clinic, hair salons, garages and a radio station. Food-oriented locales were noticeably absent. Participants valued information grounds because they interacted face-to-face with people whom they regarded as trustworthy and reliable. Information topics ranged from family issues, employment and legal help to gossip and current events. Information grounds also facilitated the phenomenon of interpersonal berrypicking. Over 700 college students were interviewed with the aim of developing an information ground typology. Findings showed that restaurants, social gatherings, places of work, the bus stop, library and dorms were students' most popular locales, while religious settings, shopping areas and hair salons were least. Over half of the students visited their favorite information ground everyday, at most all hours, and 70% had been going for over a year. Information grounds were of all sizes, though 40.5% had 2-10 participants. Students said they interacted with the same folks in other settings and that they greatly valued their information grounds for the everyday information that they obtained and social connections. Responses about how to improve information grounds emphasized increasing physical comfort and reducing barriers to social interaction. The Virtual Jaamati project uses location-specific computing at such places as bars, libraries and cafes to facilitate information sharing. The software enables patrons (using optional aliases) to connect to a wireless network using a personal computing device to gain access to services such as profiles, forums, announcement archives, photo galleries and intra-grounds Instant Messaging. We evaluated the information behavior of coffee shop patrons as they used the Virtual Jaamati software. Findings reveal ways in which the software facilitated communication and interaction among users.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,760
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0010,004
Communication savante0,0000,014
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,271
Écart entre enseignants0,260 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
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

Citations3
Publié2005
Routes d'admission1
Résumé présentoui

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