Role of classification schemes in organization of Islamic knowledge in libraries
Bibliographic record
Abstract
Purpose Classification systems play a fundamental role in the organization, display, retrieval and access to the knowledge materials in libraries. These systems have served the purpose adequately in most of knowledge areas; nevertheless, some grey areas lack proper place and enumeration in these systems. Islamic knowledge is among the areas that have not been properly addressed. The purpose of this paper is to examine this problem and indicate a potential solution. Design/methodology/approach This paper expands on the author's earlier research which focused on Pakistan library collections. Empirical data have been collected from 16 LIS scholars who have interest in or expertise on this issue through interviews. Scholars are from Pakistan, India, Malaysia, Iran, Saudi Arabia, Egypt, the UK, the USA and Canada. A review of the literature is also presented. Findings A number of approaches have been taken to work around the deficiencies of the standard classification systems when it comes to Islamic knowledge and publications, including indigenous systems and expansions. Details of some of these are presented. A range of possible improvements to existing classification systems was suggested by scholars, and an outline of what is required in a new, independent system is discussed, along with ideas about the best way for this system to be developed. Originality/value The paper discusses an area of professional concern that has been discussed widely in Islamic countries, but only in a limited fashion outside of Islamic countries. Thus, the paper should be of interest to researchers and practitioners interested in cataloguing and classification theory.
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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 | 0.046 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.029 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".