Akademik Liyakat: VOSviewer ile Bibliyometrik Bir Haritalama Analizi ve İçerik Analizi (WOS Örneği)
Bibliographic record
Abstract
This study was conducted to create a general profile of academic studies on academic merit. For this purpose, bibliometric data of different types of studies published between the years 1983 and 2024 scanned in the Web of Science (WOS) database were used. The data set was created by searching with the keyword “academic merit”, which is the English translation of the concept of “academic merit”, in the “full record” by determining the search criteria “topic”. In the findings obtained,, 112 scientific studies that met this criterion were analyzed according to the year of publication, index, number of citations, WOS categories, publication language, and most influential author collaborations and keywords. According to the findings, it is seen that most of the studies were written in 2016-2018, most of the studies are journals indexed in the Social Sciences Citation Index (SSCI), and the highest number of citations belongs to Krefting (2003) with 117 citations, according to the WOS category, most of the studies were conducted in the field of educational research. Most of the publications were made in the English language. However, in VOSviewer software, it was found that “academic merit”, “university”, and “research” were the most frequently used common keywords, the authors with the highest number of collaborations were five authors with a link strength of 8 with 17 citations for 2 studies, and the most influential countries were “USA”, “Canada” and “Austria”. Keywords: Merit, Academic Merit, Bibliometric Analysis JEL Codes: M10, M12, M14
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".