Improving Collection Development and Reference Services for Interdisciplinary Fields through Analysis of Citation Patterns: An Example Using Tourism Studies
Bibliographic record
Abstract
Analyzing the citation characteristics of the scholarly production of an interdisciplinary field according to the kind of research methodology employed can provide much valuable information that can be used to improve both collection development decisions and reference services. Focusing on tourism studies, this article shows how a detailed breakdown of citations by Library of Congress (LC) classification can help librarians manage the information scatter that is typically associated with interdisciplinary fields. Data about the percentage of cited material from particular LC classes and subclasses that are used in the collective research output of an interdisciplinary field can be helpful in identifying types of material for purchase that otherwise may be overlooked. In addition, by identifying LC classes and subclasses that generate many citations, librarians can closely examine individual citations from these classes to get a detailed sense of how interdisciplinary scholars do their intellectual work, thus allowing them to better understand and anticipate the future information needs of these scholars.
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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 | 0.044 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.044 | 0.086 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".