Digging in the Mines: Mining Course Syllabi in Search of the Library
Bibliographic record
Abstract
Abstract Objective - The purpose of this study was to analyze a syllabus collection at a large, public university to identify how the university’s library was represented within the syllabi. Specifically, this study was conducted to see which library spaces, resources, and people were included in course syllabi and to identify possible opportunities for library engagement. Methods - A text analysis software called QDA Miner was used to search using keywords and analyze 1,226 syllabi across eight colleges at both the undergraduate and graduate levels from the Fall 2014 semester. Results - Of the 1,226 syllabi analyzed, 665 did not mention the library’s services, spaces, or resources nor did they mention projects requiring research. Of the remaining 561, the text analysis revealed that the highest relevant keyword matches were related to Citation Management (286), Resource Intensive Projects (262), and Library Spaces (251). Relationships between categories were mapped using Sorensen’s coefficient of similarity. Library Space and Library Resources (coefficient =.500) and Library Space and Library Services (coefficient-=.457) were most likely to appear in the same syllabi, with Citation Management and Resource Intensive Projects (coefficient=.445) the next most likely to co-occur. Conclusion - The text analysis proved to be effective at identifying how and where the library was mentioned in course syllabi. This study revealed instructional and research engagement opportunities for the library’s liaisons, and it revealed the ways in which the library’s space was presented to students. Additionally, the faculty’s research expectations for students in their disciplines were better understood.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".