Information Professional Job Advertisements in the U.K. Indicate Professional Experience is the Most Required Skill
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".