Bibliographic record
Abstract
This paper provides an overview of the concepts of citations and journal impact factors, and the implications of these metrics for the Journal of the Southern African Institute of Mining and Metallurgy (JSAIMM). Two key research literature databases publish journal impact factors; namely, Web of Science and Scopus. Different equations are used to calculate journal impact factors and care should be exercised when comparing different journals. The JSAIMM has a low impact factor compared with some of the more prestigious journals. It nevertheless compares well with journals serving other mining sectors, such as the Canadian CIM Journal. The problems associated with journal impact factors are discussed. These include questionable editorial practices, the negative impact of this concept on good research, and the problem of a few highly cited papers distorting the journal impact factor. As a consequence, there is growing resistance to the use of journal impact factors to measure research excellence. The San Francisco Declaration on Research Assessment is a global movement striving for an alternative assessment of research quality. As a recommendation, the Editorial Board of the JSAIMM should adopt a pragmatic approach and not alter good journal policies simply to increase the journal impact factor. The focus should remain on publishing excellent quality papers. Marketing of the Journal, the quality of the published papers, and its open access policy should be used to counter the perception that journals with high impact factors are better options in which to publish good research material.
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 | 0.077 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.045 | 0.044 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.035 | 0.030 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".