Peer Review Process

The Canadian Journal of Machine Learning & Intelligent Transportation (CJMLIT) follows a rigorous double-blind peer-review process to ensure the quality and integrity of the research it publishes. Reviewers are selected based on expertise and are expected to meet the following conditions:

  • Hold a PhD or be a recognized authority in the relevant field.
  • Have no co-authored work with the manuscript’s authors in the last five years.
  • Not affiliated with the same institution as any of the authors.

Each reviewer may be invited to assess a maximum of two manuscripts per year. At least two independent reviewers evaluate each research manuscript through a double-anonymous peer-review process. One or more additional review rounds may be conducted when major revisions are required, when reviewers request verification of the revised manuscript, or when reviewer recommendations diverge. A third reviewer may be appointed when necessary. The final decision is made by the assigned editor under the oversight of the Editor-in-Chief.

Reviewer Responsibilities

  • Maintain full confidentiality of the manuscript’s content.
  • Use only the official CJMLIT Review Form to complete the evaluation.
  • Contact the editorial office before involving any co-reviewers.

Please note: unsolicited reviewer applications are not accepted. Reviewers are contacted directly by the editorial team.

Peer Review Policy

CJMLIT uses a double-blind review model. Reviewer identities are known only to the editorial board to maintain transparency and fairness while preserving anonymity for all parties.

Review Form Structure

All reviewers must download and complete the official CJMLIT review template. This form includes the following components:

  1. Recommendation for Publication:
    Reviewers must choose one of the following decisions:
    • Evaluation A: Accept as is
    • Evaluation B: Accept after Minor Revision
    • Evaluation C: Accept after Major Revision
    • Evaluation D: Reject
  2. Comments from Reviewer:
    A dedicated section for providing detailed comments and feedback on the manuscript.
  3. Evaluation Criteria:
    Each manuscript must be rated across the following dimensions:
    • Innovation: Highly / Sufficiently / Slightly / Not Novel
    • Integrality: Poor / Fair / Good / Outstanding
    • Presentation: Totally / Mostly / Partially / Inaccessible
    • Technical Depth: From Superficial to Expert-Level
    • Presentation & English: Satisfactory / Needs Improvement / Poor
    • Overall Organization: Satisfactory / Could Be Improved / Poor

Review Deadline

Reviewers are requested to return the completed review form within two weeks of acceptance. If additional time is needed, please notify the editorial office.

Download the Official Review Template

Please download and complete the CJMLIT Review Form using the following link:

Download CJMLIT Review Template (DOCX)

We thank all our reviewers for their contributions to advancing the fields of machine learning and intelligent transportation systems.