Library and Information Science (LIS) Transferable
Bibliographic record
Abstract
This article uses data obtained from a content analysis of job advertisements to explore the questions of (1) what types of non-traditional jobs are available for library and information science (LIS) professionals and (2) how can LIS students and professionals take advantage of non-traditional job opportunities. Two groups of job advertisements were used in this investigation: advertisements from LIS-targeted job boards (two library school job boards and two library association job boards) and Government of Canada internal job postings. These two sets of job postings were selected to compare the competencies in job postings targeted to LIS graduates (the LIS job board advertisements) and job postings that were not targeted to the LIS community (the Government of Canada job advertisements). An analysis of these groups of job advertisements demonstrated that both samples focused mainly on transferable competencies. Due to the emphasis on transferable competencies, the analysis of job postings from the Government of Canada job list revealed that there are many non-traditional opportunities for LIS graduates. A typical LIS professional could apply for 51 (or 25.8%) of the job advertisements in this set, having met all of the listed criteria. This individual may be able to apply for an additional 40 (or 21.2%) of the jobs listed if they had certain additional competencies or knowledge obtained through prior experience working in the Government of Canada but not necessarily obtained by the average LIS professional. This supports the argument that there are numerous opportunities for LIS professionals in non-traditional jobs. The exploration of commonly requested competencies can be used to guide LIS job seekers to craft their resumes and CVs to address the competencies requested by potential employers. Keywords competencies; job seeking; job advertisements; transferable skills; employability; professional development
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".