================================================================================ KORA JOURNAL OF APPLIED AI & MULTIDISCIPLINARY STUDIES (KJAI) 코라 응용 인공지능 및 다학제 연구 저널 REVIEWER APPLICATION FORM Published by The Korean Academy ================================================================================ HOW TO SUBMIT 1. Complete every section below. Do not delete any section; write "N/A" where something does not apply. 2. Attach your CV (PDF preferred). 3. Email both to: admission@thekoreanacad.com 4. Subject line: KJAI Reviewer Application - [Full Name] - [Country] BEFORE YOU START -- two things are required of every applicant * An ORCID iD. Registration is free and takes two minutes at orcid.org. Applications without an ORCID iD cannot be processed. * At least TWO of the eight eligibility criteria in Section F. Date of application: ____________________ -------------------------------------------------------------------------------- SECTION A -- PERSONAL AND CONTACT DETAILS -------------------------------------------------------------------------------- A1. Title (Mr / Ms / Mx / Dr / Prof / other): A2. Full name (as it should appear in the reviewer acknowledgement): A3. Preferred name form for correspondence: A4. Email address: A5. Alternative email: A6. Phone (with country code): A7. Country of residence: A8. City: A9. Time zone: -------------------------------------------------------------------------------- SECTION B -- IDENTIFIERS (B1 is mandatory) -------------------------------------------------------------------------------- B1. ORCID iD (required): https://orcid.org/____-____-____-____ B2. Google Scholar profile URL: B3. DBLP / Semantic Scholar profile URL: B4. GitHub, GitLab or other code profile URL: B5. Personal or departmental web page: -------------------------------------------------------------------------------- SECTION C -- EDUCATION -------------------------------------------------------------------------------- List your highest qualification first. Add rows as needed. Include qualifications currently in progress, marking them "in progress" with the expected year. C1. Degree / Level : Field of study : Institution : Country : Year awarded / expected : Thesis or dissertation title (if any): C2. Degree / Level : Field of study : Institution : Country : Year awarded / expected : Thesis or dissertation title (if any): C3. Degree / Level : Field of study : Institution : Country : Year awarded / expected : -------------------------------------------------------------------------------- SECTION D -- CURRENT POSITION -------------------------------------------------------------------------------- D1. Current role / job title: D2. Department or team: D3. Institution, company or organisation: D4. Country: D5. Since (month / year): D6. Sector (university / research institute / industry / government / clinical / NGO / independent): D7. If you are a student, state your programme, year of study, and the name of your supervisor (we do not contact them without asking you first): -------------------------------------------------------------------------------- SECTION E -- TECHNICAL BACKGROUND -------------------------------------------------------------------------------- E1. Which best describes you? (tick all that apply) [ ] Primarily a methods researcher (machine learning, AI, statistics) [ ] Primarily a domain researcher who applies AI in that domain [ ] Both, roughly equally [ ] A practitioner who builds and deploys AI systems in production [ ] A researcher on the ethical, legal, social or policy side of AI E2. Application domains you work in (medicine, education, engineering, climate, finance, law, linguistics, social science, other): E3. Can you assess a paper's statistics and experimental design? Yes / No E4. Can you read and evaluate released code? Yes / No E5. Programming languages and frameworks you are fluent in: E6. Can you review a manuscript written in English? Yes / No E7. Any other language you can review in: -------------------------------------------------------------------------------- SECTION F -- ELIGIBILITY (tick AT LEAST TWO, and give the evidence) -------------------------------------------------------------------------------- [ ] F1. A doctorate (completed or in progress) in computer science, artificial intelligence, statistics, engineering, or in a domain field in which you apply AI. Evidence: [ ] F2. A master's degree in one of the journal's subject areas. Evidence: [ ] F3. A bachelor's degree in a relevant field, together with demonstrable further specialisation in the area you wish to review. Evidence: [ ] F4. At least one peer-reviewed publication -- journal article, book chapter or full conference paper -- within the journal's scope. Evidence (full citation): [ ] F5. At least two years' professional experience building, deploying or evaluating AI or data systems in industry, clinical practice, government or a research group. Evidence: [ ] F6. Prior peer-review experience for a journal, conference or academic press. Evidence (name of venue; you need not name the manuscripts): [ ] F7. A substantive maintained open-source contribution, released dataset or released model within the journal's scope. Evidence (repository or archive link, and your role): [ ] F8. Recognised domain expertise -- clinical, legal, educational, engineering, policy or equivalent -- in a field where AI is being applied, including professional certification or licensure where relevant. Evidence: Number of criteria ticked: ______ (minimum 2) -------------------------------------------------------------------------------- SECTION G -- AREAS OF EXPERTISE -------------------------------------------------------------------------------- G1. Tick every subject area you are competent to review. Please be honest and conservative: reviewing outside your competence helps nobody. METHODS [ ] Machine learning, deep learning and representation learning [ ] Statistical learning theory and optimisation [ ] Natural language processing and large language models [ ] Computer vision and image analysis [ ] Speech, audio, signal and time-series modelling [ ] Reinforcement learning, planning and decision-making [ ] Knowledge representation, graphs and neuro-symbolic methods [ ] Generative models, retrieval-augmented and agentic systems [ ] Efficient, edge, federated and privacy-preserving learning [ ] Data engineering, MLOps and production deployment [ ] Evaluation, benchmarking and uncertainty quantification APPLICATION DOMAINS [ ] Health, medicine and the life sciences [ ] Education and the learning sciences [ ] Engineering, manufacturing and robotics [ ] Environment, climate, agriculture and energy [ ] Finance, economics, business and operations [ ] Law, governance and public policy [ ] Language, translation, digital humanities and cultural heritage [ ] Computational social science [ ] Security, forensics and critical infrastructure RESPONSIBLE AND HUMAN-CENTRED AI [ ] Fairness, bias and auditing [ ] Explainability and interpretability [ ] Privacy, data protection and provenance [ ] Robustness, safety and failure analysis [ ] Human-AI interaction and cognitive science [ ] AI ethics, regulation and standards [ ] Other (specify): ______________________________________ G2. Five keywords describing what you can review: G3. Methods you are competent to assess (for example: experimental design, statistical inference, causal inference, benchmarking methodology, qualitative and mixed methods, human-subject study design, systematic review method, formal analysis): G4. Would you be willing to review the code or data released with a manuscript, where the authors have released it? Yes / No -------------------------------------------------------------------------------- SECTION H -- SELECTED PUBLICATIONS OR OUTPUTS (up to five, most relevant first) -------------------------------------------------------------------------------- Full citations. Released datasets, models and maintained software count as outputs -- give the persistent identifier or repository. Write "None yet" if you have none; this does not disqualify you where other criteria are met. H1. H2. H3. H4. H5. -------------------------------------------------------------------------------- SECTION I -- REVIEW EXPERIENCE AND AVAILABILITY -------------------------------------------------------------------------------- I1. Have you reviewed for a journal, conference or press before? Yes / No I2. If yes, where, and roughly how many manuscripts: I3. How many manuscripts can you review per year? (we ask for no more than 3) I4. Any period in the coming year when you will be unavailable: I5. Would you be willing to review a manuscript at short notice if a reviewer withdraws? Yes / No -------------------------------------------------------------------------------- SECTION J -- DECLARATIONS (initial each line) -------------------------------------------------------------------------------- ____ The information given in this form is accurate and complete. ____ I will keep every manuscript sent to me confidential; I will not share it, discuss it with anyone outside the editorial process, or use its content before publication. ____ I will not upload a manuscript, its data, or its code -- in whole or in part -- to any generative AI service, hosted model or third-party tool that retains what is sent to it. I understand this is a breach of confidentiality, not a grey area. ____ I will not run, redistribute or build on code or data supplied with a manuscript for any purpose other than assessing that manuscript. ____ I will declare any conflict of interest and will decline to review the work of a close colleague, co-author within three years, classmate, student, supervisor or anyone in my own department or company. ____ I will decline promptly, rather than accept and delay, when a manuscript falls outside my competence or I cannot meet the deadline. ____ I will write reviews that are professional and constructive, and will not make personal criticism of an author. J1. Do you consent to being named in the journal's annual reviewer acknowledgement? (You may say no; it makes no difference to whether we invite you.) Yes / No J2. Do you have any connection to The Korean Academy -- as a student, alumnus, employee, teacher or contractor? This does not disqualify you; it tells us which manuscripts we must not send you. Yes / No. If yes, describe: J3. Do you have a commercial interest -- employment, equity, consultancy or funding -- in a company whose products this journal publishes work on? This does not disqualify you either; it tells us where to be careful. Yes / No. If yes, describe: -------------------------------------------------------------------------------- SIGNATURE -------------------------------------------------------------------------------- Signed: ____________________________ Date: ____________________ (A typed name is accepted for an emailed application.) -------------------------------------------------------------------------------- ATTACHMENTS CHECKLIST -------------------------------------------------------------------------------- [ ] This completed form [ ] Curriculum vitae (PDF preferred) -- required [ ] One publication, preprint, writing sample or repository link (optional, but genuinely helpful if you have no publication record yet) ================================================================================ Send to: admission@thekoreanacad.com Subject: KJAI Reviewer Application - [Full Name] - [Country] You will receive an acknowledgement within 3 to 5 working days. Successful applicants are added to the reviewer database and matched to manuscripts in their stated areas. We aim not to invite any reviewer more than three times a year. ================================================================================