With reference to Core Practices, the Committee on Publication Ethics (COPE) has established the highest standards of publication ethics, which Lectio Socialis expects all parties involved in its publication process to abide by.

The confidentiality of submitted manuscripts must be maintained. Editors cannot disclose information about a submitted manuscript to anyone except the corresponding author, reviewers, and potential reviewers.

Reviewers' Responsibilities

Authors' Responsibilities

Authors must ensure that their work is original and has not been submitted to or published in another journal.
The corresponding author must ensure that all co-authors have made significant contributions to the work and that all have agreed to the final manuscript before submission.
Authors are required to provide accurate and detailed accounts of the research conducted. Raw data must be provided upon request, and authors should be willing to make their data publicly available whenever possible.
Any ethical concerns related to research involving human or animal subjects must be properly addressed in the manuscript, and authors must provide documented evidence of ethics committee approval.
Authors are expected to disclose all sources of financial support and any potential conflicts of interest.
If a significant error is discovered in a published work, authors must promptly notify the Editor-in-Chief and cooperate in the retraction or correction of the article.
Authors are required to include an Author Contribution section, following the CRediT (Contributor Roles Taxonomy) system, detailing the contributions of each co-author.
Example:

Authors retain rights to their published materials under a CC-BY license, which allows others to copy, distribute, and adapt the work, including for commercial purposes, provided proper credit is given.

Data and Reproducibility Policy

Lectio Socialis encourages authors to follow discipline-specific standards and practices regarding data sharing and reproducibility. For clinical trials or other research requiring approval, registration with the appropriate institutional or repository bodies is mandatory. Authors are also encouraged to deposit their raw datasets in a community repository if the data is not included as supplementary material. In cases where a dataset is shared, a data availability statement should be included in the manuscript. This statement should detail the availability of the research data as well as any potential limitations due to factors such as privacy concerns or biosecurity issues. Transparent data sharing is vital to enhancing the replicability, transparency, and credibility of scientific findings.

Authors are encouraged to adhere to the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable), ensuring that all datasets and metadata are assigned a unique and persistent identifier. Lectio Socialis reserves the right to review the credibility of the dataset and may collaborate with relevant institutions to ensure the scientific integrity of the research. Compliance with COPE’s guidelines on both published and unpublished data is strictly required. Corresponding authors are expected to respond to all inquiries about their datasets. If significant issues with the dataset arise, the manuscript may be rejected. Datasets must be properly cited in the manuscript to credit the creators. The original dataset sources should be listed in the reference section and must include details such as author(s), year of publication, repository/archive name, and the dataset’s DOI.

Dataverse

Lectio Socialis now hosts replication files for published manuscripts in our Dataverse archive.

We expect authors who make quantitative inferences in their manuscripts to submit data and log files to this Dataverse archive prior to publication. We encourage authors using qualitative data to submit data to Dataverse if this would facilitate greater research transparency and accessibility. 

Lectio Socialis Dataverse Usage Guide

This guide provides step-by-step instructions for authors to submit their research data to the Lectio Socialis Dataverse repository after article acceptance. The submission process supports transparency and the replicability of published work. All articles are encouraged to include replication data, with quantitative submissions required to meet specific criteria.

For more details on the submission process and data requirements, visit the Lectio Socialis Dataverse repository here: https://dataverse.harvard.edu/dataverse/lectio/

1. Registering as a Dataverse User

- Visit the Dataverse repository and select ‘Sign Up’ or ‘Log In’ if you already have an account.

- After registration, email your registration details (excluding the password) to the Journal (lectiosocialis@gmail.com). 

2. Uploading Your Dataset

Once logged into the Lectio Socialis Dataverse, follow these steps:
- Create a New Dataset: Click the ‘+ Add Data’ button and select ‘New Dataset’.
  - Title: Use ‘Replication Data for:’ followed by your article’s title.
  - Author and Contact: Ensure these fields are prefilled with your registered information. Use the ‘Identifier Scheme’ and ‘Identifier’ fields to add your ORCID ID if applicable.
  - Description: Include the English abstract of your manuscript.
  - Subject and Keyword: Select ‘Social Sciences’ and add at least two keywords relevant to your study. These keywords will help users locate your work.
- File Uploads: Under the ‘Files’ section, upload the required files individually. Include brief descriptions for each:
  - Data file
  - Codebook or description of the dataset
  - File containing exact commands used for output
  - Statistical software output file

- Submit for Review: Once all files are uploaded, select the ‘Submit for Review’ button in the top right-hand corner.

3. Terms of Use

All datasets are shared under the Creative Commons Attribution 4.0 International License (CC BY 4.0) to facilitate easy sharing and reuse of data. If you prefer different terms, after saving your dataset, go to ‘Edit’, select ‘Terms’, and choose custom terms of use.

4. Citation Format

After uploading the data, you will receive citation details. Include this citation in the acknowledgments section of your article, formatted as follows:

> "Replication data for this article is available at: [DOI link provided by Dataverse]"

For questions about this process, please contact lectiosocialis@gmail.com.

AI-Generated Content Policy

Lectio Socialis places clear guidelines on the use of AI content-generation tools (AIGC) to ensure transparency and maintain the integrity of academic writing. While AIGC tools can be utilized for specific tasks like language refinement, their excessive use for generating entire texts is strongly discouraged.

Authors and editors are permitted to use AIGC tools for language embellishment, but they must take responsibility for ensuring the coherence of the language and the accuracy of the statements made in the text. The use of such tools must not replace significant contributions that are expected from human authors in the design, implementation, analysis, and writing of the article.

Authorship must be attributed solely to individuals who have played a meaningful role in the creation of the work, in accordance with standard academic practice. Contributions by AIGC tools or other non-human entities do not qualify for authorship. Any portion of the content generated by an AIGC tool should be disclosed transparently in the Materials and Methods or Acknowledgements sections of the manuscript. Authors should specify the reasons for using the tool, the tool's name and version, and the exact content it generated.

To ensure ethical use, the AIGC tool employed must be both stable and publicly accessible. Authors are responsible for ensuring that the tool meets these criteria and for providing clear documentation on how and why it was used.

Malpractice Policy

Lectio Socialis is committed to preventing publication malpractice and upholding the highest standards of publication ethics. The journal has strict procedures in place to address any unethical behavior, including informing the authors’ affiliated institutions about any confirmed breaches.

1. Plagiarism and Misrepresentation
All submitted manuscripts must be original works by the authors. Plagiarism, including direct copying, paraphrasing without proper citation, or using another’s ideas without acknowledgment, is strictly prohibited.
Self-plagiarism: Authors must not submit previously published work as new or submit the same manuscript to multiple journals simultaneously.
Data Misrepresentation: Authors must provide accurate research descriptions, and any falsification or fabrication of data is forbidden.
2. Conflicts of Interest
All parties—authors, reviewers, and editors—must disclose any conflicts of interest that could influence the manuscript's review or publication process. This includes financial, personal, or professional affiliations that may introduce bias.
3. Ethical Oversight
Research involving human participants or animals must follow ethical guidelines, and authors must provide documented evidence of approval by an ethics committee. Corrections or retractions will be issued in cases of ethical violations.
4. Multiple Submissions
Concurrent submissions to multiple journals are considered unethical and will result in the manuscript being rejected, and authors may be blacklisted from future submissions.
5. Citation Manipulation
Citation manipulation is prohibited. Reviewers must not suggest irrelevant citations to boost personal citation metrics unless they are academically necessary.
6. Retractions and Corrections
Retractions: Papers will be retracted if significant ethical breaches or errors are identified post-publication.
Corrections: Minor errors will be addressed through errata or corrections.
7. Response to Allegations of Malpractice
Allegations of unethical behavior will be thoroughly investigated. Authors will be allowed to respond to the claims, and appropriate corrective action will be taken if malpractice is confirmed. This may include rejection, retraction, or banning future submissions. Authors’ affiliations will also be notified in cases of confirmed breaches to ensure accountability beyond the journal’s scope.