Institutional Name Confusion on Citation Indexes: The Example of the Names of Turkish Hospitals
Bibliographic record
Abstract
Effectiveness of academia has usually been evaluated by using the number of publications. However, indexing with natural language on citation indexes makes evaluation process problematic for author affiliation. Affiliation information mistakes made by authors or editors can cause confusion during the indexing period. Consequently, visibility of the institutions in the citation indexes may reduce, different rankings for institutions may emerge and bibliometric studies may generate inaccurate results. It is important to understand these standardization problems in citation indexes to be able to fix them. The main aim of this study is to present name confusions of author affiliations within the citation indexes by using the example of the names of Turkish Training and Research Hospitals (TRH). In order to achieve this, 198,687 publications within the Web of Science, addressed in Turkey and produced between 1928 and 2009 were evaluated. The preliminary findings show that there are 65 unique TRHs which were mentioned with several different formats in the address section of the citation indexes. All these varying affiliation information were unified as a standard format for this study. It is observed that the names of these hospitals cause the main confusion. For example, there are four different “Numune TRH”s in different cities of Turkey. In such a case, if the author does not mention the city name in the address field, confusion becomes inevitable. In addition to that, affiliation information mistakes generally appear in the form of wrong spelling, abbreviation and translation mistakes. Findings of this study would bring up aforementioned confusion and present some recommendations to solve it.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.047 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".