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Enregistrement W1482285403 · doi:10.18438/b8jk6v

A Faceted Catalogue Aids Doctoral-Level Searchers

2008· article· en· W1482285403 sur OpenAlexvenueno aff
Kurt Blythe

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2008
Typearticle
Langueen
DomaineComputer Science
ThématiqueLibrary Collection Development and Digital Resources
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTest (biology)Interface (matter)Computer scienceModerationWorld Wide WebDigital libraryTag cloudPsychologyLibrary scienceLinguisticsSocial psychologyArtificial intelligence

Résumé

récupéré en direct d'OpenAlex

A Review of: Olson, Tod A. “Utility of a Faceted Catalog for Scholarly Research” Library Hi Tech 25.4 (2007): 550-61. Objective – To learn whether a faceted catalogue and word cloud aids in the discovery process. Design – User study. Setting – Large academic research library in the United States. Subjects – Twelve PhD candidates in the humanities, the majority of whom are engaged with researching, proposing, or writing their dissertations. Methods – The library’s entire catalogue of 5.2 million records was loaded into the AquaBrowser OPAC search interface. A pilot study was conducted using three humanities graduate students employed by the library. Following the pilot, the main study was conducted using graduate students in the humanities. Graduate students in the social sciences were desired for the study, but were not able to be contacted due to time constraints. Once selected, the test subjects were asked to use an interface that offered both facets and tag clouds for enhanced search quality. Test subjects were allowed to choose the topic they would like to research; all chose to research their dissertation topic. A moderator and recorder facilitated research conducted with the faceted catalogue. The moderator ensured that students commented on their findings, cleared up any confusion with using the interface, and kept the students on task. Only when students remarked that a new discovery had been made were those discoveries noted. The impact to the discovery process of faceted navigation and AquaBrowser’s word cloud was studied while the impact of relevance ranking was not. Main Results – The article asserts that results from both the pilot and main study were sufficiently similar to justify combining them for the paper, but the advantage that students employed by the library might have over other students is not discussed. Nine of the twelve students used in the study found new results using the faceted catalogue and word cloud. The responses of the user group to the faceted catalogue and word cloud were “overwhelmingly positive” (555). However, since students were allowed to move freely between the word cloud and faceted navigation tool, it is difficult to attribute new discoveries solely to one or the other. However, when a new discovery could be “attributed primarily to one factor or another” (555) it was noted. The faceted navigation tool aided discovery at least four times and the word cloud aided discovery at least six. Conclusion – A faceted catalogue interface with a word cloud feature clearly aids in the discovery process for more advanced researchers—those with specialized subject knowledge, familiarity with their library’s collection, and experience in researching their area. However, facets and word clouds have limitations: records with limited cataloguing have little to offer faceted navigation; catalogue records from diverse providers introduce controlled vocabularies beyond LCSH and MeSH into search returns, resulting in the same word potentially appearing multiple times in the same return albeit with different meanings; the word cloud may contain certain words that researchers feel to be irrelevant. Despite these issues, the use of word clouds and faceted navigation (and relevance ranking) appears to be beneficial to research conducted by experienced subject searchers in the humanities.

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)0,014
score de la tête « metaresearch » (Gemma)0,067
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,042
Score d'incertitude au seuil0,142

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,067
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0170,015
Études des sciences et des technologies0,0030,001
Communication savante0,0140,018
Science ouverte0,0010,009
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0420,028

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 Opus0,056
Tête enseignante GPT0,253
Écart entre enseignants0,197 · 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
Publié2008
Routes d'admission1
Résumé présentoui

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