Bibliometric Analysis of Articles on Collective Memory in History, Geography and Educational Studies
Bibliographic record
Abstract
The aim of this research is to reveal the bibliometric analysis of the published articles on collective memory. For this purpose, journal articles published in the Web of Science database were examined. In the Web of Science database, the keywords; collective memory" was searched in the categories of history, geography, and educational research. The study material is limited to 1986-2021 as the year, English as the publication language and SSCI, ESCI and A&HCI as indexes. The data obtained from the Web of Science database were analyzed with the bibliometrix package included in the RStudio program. Related to the subject of collective memory, annual production, the most relevant authors, the most relevant journals, the number of article productions in countries, the most frequently used sources, the most used keywords in the researches and the current trends in the articles on the subject of collective memory were determined in the journal articles. As a result of the research, it was identified that in recent years, when the articles written on the subject of social memory in history, geography and education researches have been increasing and diversifying, issues such as commemorative culture, space, violence, power and politics have created a trend towards this field. It was determined that the most relevant countries for the subject area were the USA, England, Israel, Germany and Canada. In the articles written on collective memory, it was determined that the most frequently used keywords of the researchers were history, politics, identity, memory, war, place, holocaust, and commemoration culture. It is thought that these results will give perspective to researchers who plan to conduct research in this field.
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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.199 | 0.244 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".