The Library of Congress, Dewey Decimal, and Universal Decimal Classification Systems are Incomplete and Unsystematic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective – To determine the extent to which knowledge is currently addressed by the Library of Congress (LCC), Dewey Decimal (DDC), and Universal Decimal (UDC) classification systems.
 
 Design – Comparative analysis of the LCC, DDC, and UDC systems using Zin’s 10 Pillars of Knowledge.
 
 Setting – The Faculty of Philosophy and Science at a Brazilian university.
 
 Subjects – Forty one subject-related classes and 386 subclasses from the first two levels of the LCC, DDC, and UDC systems. 
 Methods – To evaluate the LCC, DDC, and UDC systems, the researchers employed the 10 Pillars of Knowledge, a “hierarchical knowledge tree” developed by the lead author of this study (p. 878). According to the authors, the 10 Pillars of Knowledge seek to illustrate relationships between fields of knowledge while capturing their breadth. The first level of the Pillars consists of the following categories: Knowledge, Supernatural, Matter and Energy, Space and Earth, Nonhuman Organizations, Body and Mind, Society, Thought and Art, Technology, and History. Each of the 10 Pillars is further subdivided, resulting in a four level hierarchical structure of 76 categories. Of the 76 categories, 55 are unique subject areas. A selection of subject-based classes and subclasses from the first two levels of the LCC, DDC, and UDC systems were then mapped to the relevant subclasses within the Pillars. Analysis was limited to the first two levels of LCC, DDC, and UDC, except for the LCC categories of BF and BL where further subclasses were analyzed. Classes or subclasses in LCC, DDC, or UDC that were not subject based (for example, those based on publication type) were excluded from the study. In total, 41 main classes and 386 subclasses from LLC, DDC, and UDC were categorized using the 10 Pillars.
 
 Main Results – The LLC, DDC, and UDC systems were deemed to be complete and systematic in their coverage of only three of the 10 Pillars: Matter and Energy, Thought and Art, and History. This means that there was at least one class or subclass in each of the three systems that corresponded to the subclasses in these pillars. The remaining seven pillars were only partially covered by the three systems to varying degrees. For example, the coverage of religion in LCC and DDC show evidence of a bias towards Christianity and incomplete coverage of other faiths. In addition to the lack of completeness in terms of subject coverage, the researchers found inconsistencies and problems with how relationships between subjects were illustrated by the systems. For example, botany should be a subclass of biology, but the subjects occupy the same level in the LCC, DDC, and UDC systems. Researchers also noted cases where subclasses on the same level were not mutually exclusive e.g., the BR (Christianity) and BS (The Bible) subclasses in LCC. Overall, LLC performed slightly better than DDC or UDC, covering 47 of the 55 unique subject categories in the 10 Pillars. It was followed by UDC with 44 out of 55, and DDC with 43 out of 55. Some of the 55 unique subject categories in the 10 Pillars system were not represented by any of the systems: 3 subclasses under Society (Society at Large – Area Based, Social Groups – Age, and Social Groups – Ethnicity), 2 under Technology (Technologies – Materials and Technologies – Processes), and 1 under Foundations (Methodology).
 
 Conclusion – The researchers conclude that none of the three major classification systems analyzed provides complete and systematic coverage of the world of knowledge, and call for the library community to move to new systems, such as the 10 Pillars of Knowledge.
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.
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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.350 |
| 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 it