Getting published: achieving acceptance from reviewers and editors
Bibliographic record
Abstract
Purpose – The purpose of this paper is to develop an understanding how to successfully develop, write up and get research work accepted and published in an English language business research journal. Design/methodology/approach – A review of good basics in writing, producing good scholarly academic writing, presentation of papers that are in a style that is proper for academic English language journals and how to avoid common writing problems and mistakes. Findings – Getting research work published requires persistence, people and progress. One must have persistence in seriously approaching and improving one’s research work. Researchers need to involve a network of people (conference attendees, people who understand the area, reviewers and editors) to develop good research. Research should lead to progress in our understanding of the way the world works. Practical implications – This paper helps authors readily bring their research to publishable quality in English language research journals by reducing pitfalls to authors writing in English as a foreign language. Originality/value – By providing not only sound practical advice, and how to avoid potential errors, the paper also provides graphic diagrams and a checklist for research writing that will aid authors writing in English as a foreign language in readily bringing their research to publishable quality in English language research journals.
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 | 0.323 | 0.775 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.048 | 0.034 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.041 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".