Bibliographic record
Abstract
Abstract Purpose – Canadian library workforce data were used to explore recent graduates’ perceptions of their MLS programs: their ratings of the competencies acquired, satisfaction with the overall quality of education, and suggested improvements. Design/Methodology/Approach – Surveys of libraries and practitioners were conducted from 2003 to 2006. These data were used as a baseline in replicating the survey with the staff of Canadian research libraries in 2013/2014. Recent graduate librarian data were extracted from the two data sets and comparatively analyzed. Findings – The profile of recent graduates did not change appreciably between 2004 and 2014. Graduates surveyed in 2014 more favorably rated generalist skills and were more likely than the 2004 sample to indicate that they were provided with the range of skills and abilities required to effectively perform their jobs. Management, leadership, and business skills continued to rank lowest. Roughly half of 2004 and 2014 graduates continued to indicate satisfaction with the quality of education received overall. Similarly, half of 2004 and 2014 graduates felt that they could apply what they learned to their current jobs and fewer agreed that they were provided with a realistic depiction of what it is like to work as an academic librarian. Suggestions for program improvement were mostly stable over time, with greatest importance attached to making programs more practical/practice-oriented and improvements to the relevance and currency of the curriculum. Originality/Value – Studies of the Canadian library workforce had not been conducted previously. This study should be of interest to MLS schools who are re-envisioning their programs with the experiences of recent graduates/new professionals in mind.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".