SLIS Student Research Journal, Vol.7, Iss.1
Bibliographic record
Abstract
This issue of the SLIS Student Research Journal features two articles addressing schema, structure, and theory in LIS.In an invited contribution, Dr. Mary Bolin proposes applying linguistic theories and frameworks to LIS research.Bolin states that librarians "already recognize the significance of the language that we use" (p.1), and suggests that interdisciplinary methods may strengthen investigations in areas of LIS research concerned with semantic structures and communicative events.Bolin suggests numerous convergences between the disciplines: how typology may be used for parsing qualitative data, or semantic frames for examining relationships and meaning in metadata schemas; discourse analysis and genre theory also offer intriguing possibilities for examining user communities in library contexts.In our peer-reviewed section, MLIS candidate Chloe Noland evaluates the interoperability of Library of Congress Classification and Elazar at two libraries of the American Jewish University.Noland compares bibliographic metadata from the two collections, considering semantic accuracy and user impacts.Noland determines that although in the academic context it is unclear which classification system may be preferable, "for purposes of Jewish themes and subjects…Elazar overwhelmingly provides the best specificity" (p.12).This article will be of interest to special collections librarians and cataloguing and metadata specialists.This thirteenth issue of the SRJ closes my tenure as Editor-in-Chief with the journal.It has been a year of significant development in organizational planning, yielding a refreshed strategic plan for the SRJ, revisions to our recruitment, orientation, and training for editors, and the launch of a new peer-reviewed reviews section for the journal.These accomplishments build on the work of 50 student editors who have contributed to the SLIS Student Research Journal since its establishment in 2010.Student editors have designed the journal's aims and scope, planned operations, reviewed and edited manuscripts, promoted the SRJ to a diverse readership, and built the journal's reputation.The SRJ is distinct as the only student governed, double-blind peer reviewed MLIS journal in North America publishing graduate student scholarship.Strong operations and governance at the SRJ have produced more than 50 refereed articles over seven years.However, the SRJ has also provided a unique forum for MLIS candidates to experience scholarly communication as editors, reviewers, teammates, and managers.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.025 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.141 | 0.094 |
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".