(Korean translation): 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. This guide was translated into Korean by scholars at the National Research Foundation (NRF) of South Korea.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Evaluation · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".