Bibliometric Study of Chemistry Literature in North Eastern Hill University During 2000 to 2010
Bibliographic record
Abstract
The main purpose of the study is to find out the scattering of Chemistry periodical literature in ‘North Eastern Hill University (NEHU)’ using bibliometric studies and to identify the core journal in this field. For the purpose of this study faculty members from Chemistry department have been chosen which comprise of nineteen faculty members and articles published by them in research journals during the period 2000 to 2010. The articles included in the present study were collected from NEHU institutional repository, Developing Library Network (DELNET) via Document Delivery Service (DDS) and also from ‘Web of Science (WoS)’ database of the Institute for Scientific Information (ISI). A total of 377 journals containing 4134 references were collected, MS-Excel spreadsheet and MS-Word were used to analyse the final data collected in order to generate tables, charts, graphs, etc. From the growth of literature on the subject of Chemistry the analysis of data showed that the nature of growth literature is not consistent as the number of publication varies in nature. In identification of core journals Leimkuhler model was employed and the following relationship of each zone is 5: 41 : 331 377 which fit into Bradford’s distribution.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.047 | 0.081 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".