The value of liberal arts education for finding professional employment
Bibliographic record
Abstract
Purpose Debates about the role of liberal arts education in finding employment highlight both its benefits and the challenges of finding work after graduation – debates that are now well-documented and outlined in this paper. Adding to these debates, the purpose of this paper is to bring in the voices of recent graduates from social sciences and humanities programs who have firsthand and recent experience as they enter the professional job market. Their experiences guide our understanding of the nature of liberal arts programs and shed light on areas of improvement in line with improved career paths and employment outcomes. Design/methodology/approach The methodology involved a quantitative data study using an online survey completed by 1,901 graduates. Findings A survey completed by 1,901 graduates of IDS programs in Canada provided rich data about the challenges and opportunities of their education in relation to professional employment. Additional follow-up qualitative data provided by survey participants was also analyzed. Practical implications From these findings, several implications for curriculum design are highlighted to strengthen (not replace or alter) existing program offerings. Implications for curriculum design: The quantitative data and narrative responses from the survey of IDS graduates on their career paths highlight several important considerations for IDS and other liberal arts programs that are grappling with questions about whether and how to redesign curricula to better address concerns about the employability of students. Social implications The central lesson from this research is that the perspectives of university graduates can provide valuable insights for debates about the roles of universities and the design of university curricula. While the voices of university administrators, professors, politicians, industry leaders and media pundits are all prominent in these debates, the perspectives of graduates are often left out, despite their firsthand experience in making the transition from campus to career. Originality/value This research project offers one model that other fields of study could follow to learn more from their graduates about the competencies and skills which they most value in navigating complex career paths and overcoming barriers to professional employment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".