Conference to Journal Article Pipeline: Myth or Muscle?
Bibliographic record
Abstract
One of the busiest times of the year for management-related conferences is upon us.As you book your travel arrangements and prepare for the meetings, you may be considering whether to develop your SoTL research and instructional innovations into journal submissions.In this editorial, we explore this process.We begin with a brief overview of helpful practices from literature, followed by some personal reflections.Next, we highlight some upcoming manuscript development opportunities at JME.Finally, we close with an introduction to the articles featured in this issue, including our first contribution to the Interviews section. Conference Paper to Journal ArticleAs Mainous and Saultz (2021, p. 750) note, "there are many benefits of attending scientific conferences" and presenters, for various reasons, may not always pursue an eventual publication of their submission.The limited studies on the topic indicate for some health sciences a 33% to 50% conversion rate of conference presentation to publication (Mainous & Saultz, 2021;Turale, 2015).The hope for this editorial is to provide some support for authors who wish to do so.As an aside, we were unable to find analogous data in management education and flag this as a research opportunity.
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 | 0.084 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.136 | 0.052 |
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".