Information Professional Job Advertisements in the U.K. Indicate Professional Experience is the Most Required Skill
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,006 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».