The government needs more librarians: The applicability of an MLIS education in a public sector setting
Bibliographic record
Abstract
Despite seemingly aligned information-related objectives and geographic proximity, the employment intersection between graduates of the University of Alberta’s ALA-accredited MLIS program and the Government of Alberta, a major provincial public sector employer, has been limited. Seeing an opportunity for MLIS graduate employment with the GOA, this research builds from an analysis of recruitment postings complemented with survey and interview findings from MLIS graduates now working at the Government of Alberta. The information garnered addresses how their MLIS prepared them for their work, where there were gaps, and what, if anything, they would have done differently to prepare for a public service career. \n\nDiscussion focuses on the education, experience, and competencies sought by this public sector employer. Covering multiple job levels, Government of Alberta recruiters often expressed a preference for a “library education” but it was seldom a mandatory requirement, nor was a masters-level education. Every job required additional experience or expertise, indicating that MLIS graduates interested in public sector work may have to develop additional experience elsewhere or be prepared to accept a lower-level entry position. Information work in a government setting is not fundamentally different from traditional\nlibrarianship focused on public or academic institutions where, at the core, the aim is to make information accessible for the public good. However, findings indicate that the government employee is often required to further analyze information to support decision-making, requiring skills and competencies that many reported underdeveloped in their MLIS education including project management; business analysis and writing skills; technology; and policy 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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".