Writing a Publishable Paper – Principles, Questions, Realities, and Stumbling Blocks
Bibliographic record
Abstract
Purpose: This paper identifies what business faculty, new PhDs, graduate students, practitioners, and other scholars can do to improve the likelihood of publishing their research in academic journals.Design/methodology/approach: Despite the fact that well educated scholars are generally well informed about management principles and theories, the success rate for obtaining acceptance of papers submitted to academic journals is typically well below 50% -- with some journals rejecting up to 95% of papers submitted.Findings: This paper identifies principles important to writing an academic paper, questions to address to ensure that those papers achieve a high standard, realities that impact the publishing process, and stumbling blocks to overcome to get a publication accepted.Originality/value: This article highlights the overlooked concept of flair factors in the selection process, identifies six factors that are vital for successful employee selection, suggests three tools to improve selection processes, and presents five propositions for practitioners and scholars.
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 | 0.414 | 0.626 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.046 | 0.024 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".