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Enregistrement W1854042962 · doi:10.18438/b8g597

Use and access of grey literature in special libraries may be hindered by lack of visibility and cataloguing

2006· article· en· W1854042962 sur OpenAlexvenueno aff
David Höök

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2006
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueOptics and Image Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLibrary scienceGrey literatureGovernment (linguistics)Presentation (obstetrics)PublishingCollection developmentSociologyPublic relationsPsychologyComputer sciencePolitical scienceMedicineMEDLINELaw

Résumé

récupéré en direct d'OpenAlex

A review of: Ranger, Sara L. “Grey Literature in Special Libraries: Access in Use.” Publishing Research Quarterly 21.1 (Spring 2005): 53-63. Objective – To examine the barriers to making grey literature (literature not controlled by commercial publishers) easier to access in special libraries. Design – Interviews. Setting – Variety of special libraries (government, corporate and specialized academic) in the United States. Subjects – Sixteen librarians from fourteen organizations in Washington, Michigan and Texas were interviewed. Four of the organizations were government libraries, four were corporate libraries and five were specialized academic libraries. One of the interviews was not used because the organization did not maintain a collection of paper-based grey literature. Methods – Librarians were selected as possible interview subjects via three methods: some were previously familiar with the author; some were referred to the author by friends, family and colleagues; two candidates volunteered in response to a presentation of the project at a professional meeting. Interviews were conducted between February 2002 and May 2003. A standard set of seven questions were used, but often followed with further questions. The interviews were conducted either in the library or the librarian’s office. The interviews were tape-recorded and the answers were written down. Interviews typically lasted between fifteen and thirty minutes and asked about the current state, holdings, access and use of grey literature in the special library. Main Results – Results from the interviews suggest a wide variance in the percentage of users that access grey literature. Grey literature was used less in the corporate libraries than the academic and government libraries. The percentage of the collection made up of grey literature also varied widely between the different libraries. Reports were found to be the most popular form of grey literature, although most of the libraries reported owning conference proceedings and newsletters in addition to reports. One interesting observation found during the interviews was that most of the users of grey literature are also producers of grey literature. The librarians surveyed reported that some of the reasons for using grey literature included use in research, to write (often more grey literature), interest in the topic, for class assignments, as records of previous practices, for localized studies, and for creating models and practices. Results found that for the libraries surveyed, much of the grey literature remains uncatalogued and what has been catalogued was done using a variety of methods. Over half of the libraries surveyed had their grey literature accessible online. Conclusion – Two main reasons were cited as explanations for why grey literature was not used as much as it should be: lack of cataloguing and visibility. In many of the libraries surveyed, much of the grey literature had not been catalogued, making it difficult to find and use the resources. Reasons cited for not cataloguing grey literature include lack of time, funds and/or knowledge. As well, in many of the libraries surveyed, it was found that the holdings of grey literature were not readily visible to the users, so users were not even aware that it existed. To improve the awareness and accessibility of grey literature, the author recommends regional depositories for grey literature, international standards for cataloguing and more cooperation between special libraries.

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)0,048
score de la tête « metaresearch » (Gemma)0,160
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,971
Score d'incertitude au seuil0,256

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0480,160
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0250,021
Études des sciences et des technologies0,0080,013
Communication savante0,0290,033
Science ouverte0,0020,020
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0180,004

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 Opus0,026
Tête enseignante GPT0,261
Écart entre enseignants0,236 · 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.

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

Citations1
Publié2006
Routes d'admission1
Résumé présentoui

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