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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".