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Enregistrement W1585330165 · doi:10.18438/b8ts51

Information Professional Job Advertisements in the U.K. Indicate Professional Experience is the Most Required Skill

2009· article· en· W1585330165 sur OpenAlexvenueno aff
Stephanie Schulte

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2009
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Information Literacy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContent analysisJob analysisComputer sciencePsychologyMedical educationSociologyMedicineJob satisfactionSocial psychology

Résumé

récupéré en direct d'OpenAlex

A Review of: Orme, Verity. “You will be…: A Study of Job Advertisements to Determine Employers’ Requirements for LIS Professionals in the UK in 2007.” Library Review 57.8 (2008): 619-33. Objective –To determine what skills employers in the United Kingdom (U.K.) want from information professionals as revealed through their job advertisements. Design – Content analysis, combining elements of both quantitative and qualitative content analysis. Orme describes it as “a descriptive non-experimental approach of content analysis” (62). Setting – Data for this study were obtained from job advertisements in the Chartered Institute of Library and Information Professional’s (CILIP) Library and Information Gazette published from June 2006 through May 2007. Subjects – A total of 180 job advertisements. Methods – Job advertisements were selected using a random number generator, purposely selecting only 15 advertisements per first issue of each month of the Library and Information Gazette (published every two weeks). The author used several sources to create an initial list of skills required by information professionals, using such sources as prior studies that examined this topic, the Library and Information Science Abstracts (LISA) database thesaurus, and personal knowledge. Synonyms for the skills were then added to the framework for coding. Skills that were coded had to be noted in such a way that the employer plainly stated the employee would be a certain skill or attribute or they were seeking a skill or a particular skill was essential or desirable. Skills that were stated in synonymous ways within the same advertisement were counted as two incidences of that skill. Duties for the position were not counted unless they were listed as a specific skill. Data were all coded by hand and then tallied. The author claims to have triangulated the results of this study with the literature review, the synonym ring used to prepare the coding framework, and a few notable studies. Main Results – A wide variety of job titles was observed, including “Copyright Clearance Officer,” “Electronic Resources and Training Librarian,” and “Assistant Information Advisor.” Employers represented private, school, and university libraries, as well as legal firms and prisons. Fifty-nine skills were found a total of 1,021 times across all of the advertisements. Each advertisement averaged 5.67 requirements. These skills were classified in four categories: professional, generic, personal, and experience. The most highly noted requirement was professional experience, noted 129 times, followed by interpersonal/communication skills (94), general computing skills (63), enthusiasm (48), and team-working skills (39). Professional skills were noted just slightly more than generic and personal skills in the top twenty skills found. Other professional skills that were highly noted were customer service skills (34), chartership (30), cataloguing/classification/metadata skills (25), and information retrieval skills (20). Some notable skills that occurred rarely included Web design and development skills (6), application of information technology in the library (5), and knowledge management skills (3). Conclusion – Professional, generic, and personal qualities were all important to employers in the U.K.; however, without experience, possessing these qualities may not be enough for new professionals in the field.

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)0,004
score de la tête « metaresearch » (Gemma)0,023
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 enseignants0,043
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,023
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0050,010
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0160,006

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 Opus0,016
Tête enseignante GPT0,323
Écart entre enseignants0,307 · 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
Publié2009
Routes d'admission1
Résumé présentoui

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