PUTAJ- Humanities and Social Sciences: A Ten Years Bibliometric Analysis
Bibliographic record
Abstract
The Peshawar University Teachers Association Journal (PUTAJ) is an HEC recognized journal published by the University of Peshawar since 1993/94. This study examined 276 articles published in PUTAJ-Humanities and Social Sciences during 2007-2016 in its 10 volumes and 13 issues using bibliometric analysis. For this purpose, standard bibliometric features including authorship patterns with the amount of productivity, organizational affiliation, collaboration, and country of origin were analyzed. In addition, the topics and year-wise distribution of articles, number of citations appended, and the number of pages per article published in the journal were also analyzed. For determining the subject of the articles the keywords given in the abstracts of the articles were used. The data was collected directly from the articles and entered into a database that was formed in MS-Access. The reports were generated with the help of a database according to the objectives of the study and then the data were transferred to MS-Excel for calculating percentages. The findings revealed that the majority of authors (65.58%) contributed one paper in PUTAJ-Humanities and Social Sciences either individually or in collaboration. The number of multi-authored papers was high with 216 (78.24%) papers out of 276. The researcher from the University of Peshawar published the maximum number of papers (67.78%). The highest number (99%) of authors belonged to Pakistan. In foreign contributions authors from Malaysia were on the top of the list with 0.45 percent, followed by authors from Canada with 0.15 percent. Education, English, Economics, Psychology, and Library and Information Science were the most main subjects of Humanities and Social Sciences in which articles have been published with a total of 53.23 percent. Approximately 28 articles have been published per year.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.090 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".