P (2003) Providing a technology edge for liberal arts students
Bibliographic record
Abstract
Students ’ employability has long been a challenging issue for many liberal arts colleges and universities. There has been widespread recognition recently that liberal arts students are highly valued as employees. But there is also a public perception that liberal arts students may not be well equipped to face the challenges of employment in the information age. This study is a collaborative effort among three Canadian universities: the University of Alberta, the University of British Columbia, and the University of New Brunswick. At these universities students were surveyed across the liberal arts disciplines, which were defined broadly to include the fields of fine arts, the humanities, and the social science disciplines. (In the remainder of this paper, we use the term “arts ” to include all of the liberal arts.) The focus of investigation was the popular perception that arts students “have fallen behind, ” or are languishing on the wrong side of a “digital divide ” with respect to their computer skills, and as a consequence are at a disadvantage when it comes to employment success immediately after graduation. This research served as the first phase of a two-year project that aims to address the computing skills gap in liberal arts curricula and to provide a technology edge for students ’ employability. This Technology Edge project will accomplish the following set of goals in three phases: Phase I: Needs Assessment • survey the differences in information technology (IT) competencies between 4 th year liberal arts and non-arts students • solicit detailed descriptions of IT competencies from current arts employers
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".