ENGINEERING STUDENTS AND THE LIBRARY - FORGING THAT CRITICAL FIRST LINK
Bibliographic record
Abstract
Engineering librarians occasionally experience difficulty getting an initial “in,” that all-important first connection with engineering students and faculty. In part, that may be because many do not have engineering degrees themselves. That said, a fundamental question arises- where and how can they make that initial contact? Equally important, how can the goals of the engineering faculty and the Library support one another? Engineering schools usually have a first year course intended to introduce broad aspects of the profession, and to show that Engineering does not exist in isolation. Rather, it interacts with and impacts upon society in a multitude of ways. The Canadian Engineering Accreditation Board mandates those same goals, and also requires that students learn to communicate adequately, both orally and in writing, and work effectively as part of a project team. The University of Regina Faculty of Engineering uses two first year classes to begin this process. It is at that point, in the first semester, that a connection between incoming students and the Library starts to be forged.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.033 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.010 |
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".