Avoiding Predatory Journals and Questionable Conferences: A Resource Guide
Bibliographic record
Abstract
Purpose: The goal of this guide is to provide a clear overview of the topics of predatory journals and questionable conferences and advice on how to avoid them. This guide intentionally adopts a plain language approach to ensure it is accessible to readers with a variety English language proficiency levels. Methods: Electronic searches were conducted manually using Google and Google Scholar, along with a search of the University of Calgary library research databases. Search terms included predatory journals, predatory publisher, predatory conference, questionable conference and vanity conference. Three primary types of sources informed this report: (1) scholarly peer-reviewed articles; (2) reputable popular media such as established newspapers; and (3) grey literature such as blogs written by experts and scholars. Findings: Plain-language overviews of predatory publications and questionable conferences are provided to help researchers understand what these are and how to avoid them. A discussion of how to figure out where an aspiring author should publish their work is included, as well as a checklist for determining if a conference is worth the prospective presenter’s time and resources. Implications: There are implications for mentors of graduate students and early-career stage academics, as well as for institutions as a whole. The issue of questionable conferences and publications is so complex that early-stage academics require support and mentorship to cultivate a deeper understanding of how to share their work in a credible way. Additional materials: Contains 66 references and 2 tables.
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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.055 |
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".