South African Journal of Science: Writing with integrity workshop (2024)

A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.

1. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024
How to cite:
Presenter’s last name, initials. Title of presentation [PowerPoint slides].
2024. Available from: URL
© 2024. The Author(s). Published under a
Creative Commons Attribution Licence.

2. Using AI responsibly in your writing
Kirstin Krauss
https://www.linkedin.com/in/kirstin-krauss
https://academy.wwis.co.za/
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024

3. © KEM Krauss
2
In any scientific endeavour, a researcher should be
able to carefully craftscientific arguments in
writing.
Scientific argumentation begins with research
problems/questions and an appropriate conceptual
basis in the form of a review of the right literature.
Quality scientific arguments consist of specific
claims that should be developed and presented with
internal consistency so that examiners, reviewers,
and readers can easily follow the logic of scientific
inquiry.
Defining scientific knowledge production
Scientific argumentation
Relevant knowledge
gaps
Reading the right
literature safely
Articulating the logic of
inquiry

4. © KEM Krauss
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Agenda
•Using AI when reading, reflecting, generative writing, & internalising literature
•AI & principles of scientific argumentation
•AI & Theoretical elaboration
•AI & its worldview bias
•AI & principles of text analysis
•AI & proofreading
•Articulate your use of AI as part of the scientific argument

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Using AI when reading,
reflecting, generative
writing, & internalising
literature

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Systematic
Rigour
Trustworthy, quality,
peer-reviewed
Tracing thematic
networks of papers
Trusted & curated
databases
Avoidance
strategies
AI ‘interpreted’,
summarised &
paraphrased
Finding seminal
papers & authors
•Open access training
data
•Relying on available &
accessible sources
Relevant sources
Forwards &
backwards
referencing
Traditional
approaches
AI-driven,
AI-complemented
AI & literature reviews
Reading assistance

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3. Summarise &
Reflect
1. Identify
2. Organise
4. Scientific peer-
review

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The generic literature review process
Preparing
Identifying a literature
review methodology
Purpose statement
Alignment with research
questions
Searching & Organising
Defending a literature
review approach
Search strategies
Avoidance strategies
Organising strategies
Reading & Reflection
Generative writing
Writing as a form of
thinking
Brainstorming ideas
while reading
Contextualisation,
particularising
Generating ideas,
internalising existing
knowledge
Drafting& Editing
Argumentation
Structuring & Flow
Constructive
Alignment
Informing research
directions

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Don’t outsource thinking to an AI tool
•Little knowledge will be internalised
•Reflection on findings will be difficult if not impossible
•It will be difficult to innovate or add to the body of knowledge

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AI & principles of
scientific
argumentation

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“Understanding the principles of scientific
knowledge production is becoming our
only robust defence against questionable
scientific trends”
Predatory
publishing
Citation
pollution
Citation
cartels Paper
mills
Bad
science
Low quality
researchWeak peer-
review
Plagiarism
Copyright
issues
Falsified
data
AI
generated
content

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In any scientific endeavour, a researcher should be
able to carefully craftscientific arguments in
writing.
Scientific argumentation begins with research
problems/questions and an appropriate conceptual
basis in the form of a review of the right literature.
Quality scientific arguments consist of specific
claims that should be developed and presented with
internal consistency so that examiners, reviewers,
and readers can easily follow the logic of scientific
inquiry.
Defining scientific knowledge production

13. © KEM Krauss
12Rhetoric
Relevance
Reality
Rigour
Weber, R., 2009. Research on ICT for development: Some reflections on
rhetoric, rigor, reality, and relevance [Keynote address]. In International
Development Informatics Association Conference (IDIA) 2009 (pp. 2-27).
Monash University Publishing.
Rhetoric should be sustained by research findings
(Weber, 2009)
“Without research rigor, useful
[Accurate]models of realityare difficult
to build”
(Weber 2009)
“Relevance… is problematic,
because it is unclear whether they
can be sustained by the research
that underpins them”
(Weber 2009)
Scientific
Integrity &
Research Ethics

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Elements of an argument
Claim–testing of
the destination
Grounds–required foundation if
solid / reliable
Warrant –testing of
grounds; judging solidity,
reliability
Backing of warrant
(authorised arguments)
•What exactly are we discussing?
•Where precisely are we to stand on
this issue?
•And what position must we consider
agreeing to as the outcome of the
argument?
•What information(or facts) are you
going on?
•What groundsis your claimbased on?
•Where must we ourselves begin if we are
to see whether we can take the steps
you propose and so end by agreeing to
your claim?
•Given the starting point, how do you justify
the move from these groundsto that claim?
•What road do you take to get from this
starting point to that destination?
•Is this really a safe move to make?
•Does this route take us to the required
destination securely and reliably?
•And what other general information do you have
to back up your trust in this particular warrant?
Toulmin, S. E., R. Rieke and A. Janik: 1984,
An Introduction to Reasoning, 2nd edition,
Macmillan, New York

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Outsourcing the responsibility of argumentation
to AI
•The logic of scientific inquiry is hidden and untraceable
•Readers cannot corroborate or replicate the process
•Are we going to see a publication divide?

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AI & Theoretical elaboration
“Theory is what we do" as scientists
It is about “explanation requirements”

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Defining theory
•
How can theory be defined?
•
‘the most cited definitions is that of Bacharach (1989, p. 496) who defines
theory as “a statement of relations among concepts within a set of
boundary assumptions and constraints. It is no more than a linguisticdevice
used to organize a complexempirical world.“’ (p. 2)
•
“Stinchcombe(1987) describes this notion of theory best when he explains
how great researchers inventexplanationswhen they are confronted with
phenomena and data that they cannot explain.” (p. 2)
•
‘Popper (1959, p. 59) reflects this understanding of theory when he says
that "theories are nets cast to catch what we call 'the world': to
rationalize, to explain, and to master it. We endeavorto make the mesh
ever finer and finer."’
Hassan, N.R. and Lowry, P.B., 2015. Seeking middle-range theories in
information systems research. Proceedings of the Thirty Sixth International
Conference on Information Systems, Fort Worth

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Propositions
•Regardless of their emphasis on explaining and predicting or on interpreting
and understanding, they all agree that the more empirically validated
propositions are, the better the resulting theories organize, understand,
explain, or predict (i.e., solid evidence, reliable claims)
•In building theory (theorising), the goal is to get propositions to the point
where their claims can be fully tested empirically, thereforecompleting the
link between the theory and its empirical content.
Hassan, N. 2014. “Useful Products in Theorizing for Information Systems,” Thirty
Fifth International Conference on Information Systems, Auckland, New Zealand,
2014. [Online] http://aisel.aisnet.org/icis2014/proceedings/ResearchMethods/5/

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Making data-theory links using AI
tools
•Transcribe the data (auto transcribed)
•Extracted themes from the data
•Aligning the extracted themes to the research questions
•Getting consistency & rigour in ‘analysis’
•Can I consistently get the same themes from prompting?
•Were the contextually correct themes identified?
•Should I use all the themes ‘generated’?
•Extracting relevant quotes from the transcriptions
•Can I connect my discussion and findings to a theory?
•Any worldview biases?

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Don’t outsource theoretical elaboration

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AI & its worldview bias

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https://www.ucc.ie/en/cirtl/projects/national/ai2edartificialintelligenceacademicintegrity/

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•Detectors consistently misclassify non-native English writing samples as AI-
generated
•“GPT detectors are inadvertently penalizing individuals with limited linguistic
proficiency.”
•Ethical implications of deploying ChatGPT content detectors and caution against
their use in evaluative or educational settings
https://www.cell.com/patterns/fulltext/S2666-3899(23)00130-7

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AI Pollution / Dilution
•"Scientists warn of AI collapse“
•"The more AI eats its own output, the less
variety the output has"
•"Its like plastic pollution, it won’t be long
before we eat and breath this stuff"
•"AI needs human creativity“
https://www.youtube.com/watch?v=NcH7fHtqGYM

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Are you inviting a dominant or foreign
worldview into your research situation?

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AI & principles of text
analysis

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Hermeneutics –principles of text
analysis
•Hermeneutic circle:
•“we come to understand a complex whole from preconceptions about the
meanings of its parts and their interrelationships.”
•Contextualisation:
•“Requires critical reflection of the socialand historicalbackground of the
research setting, so that the intended audience can see how the current
situation under investigation emerged” … and then how to respond in the
act of excavating data from the situation
Klein, H.K. and Myers, M.D.,
1999. A set of principles for
conducting and evaluating
interpretive field studies in
information systems. MIS
quarterly, pp.67-93.

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Hermeneutics –principles of text
analysis
•Interaction Between the Researchers and the Subjects:
•“Requires critical reflection on how the research materials (or "data") were socially
constructed through the interaction between the researchers and participants”
(Intersubjectivity)
•Abstraction and Generalization (i.e., particularising):
•“it is important that theoretical abstractions and generalizations should be carefully
related to the field study details as they were experienced and/or collected by the
researcher. This is so readers can followhow the researcher arrived at his or her
theoretical insights.”
Klein, H.K. and Myers, M.D.,
1999. A set of principles for
conducting and evaluating
interpretive field studies in
information systems. MIS
quarterly, pp.67-93.

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Hermeneutics –principles of text
analysis
•Dialogical Reasoning
•“The most fundamental point is that the researcher should make the historical intellectual
basis of the research (i.e., its fundamental philosophical assumptions) as transparent as
possible to the reader and himself or herself.”
•Multiple Interpretations
•“Requires sensitivity to possible differences in interpretations among the participants as
are typically expressed in multiple narratives or stories of the same sequence of events
under study. Similar to multiple witness accounts even if all tell it as they saw it.”
Klein, H.K. and Myers, M.D.,
1999. A set of principles for
conducting and evaluating
interpretive field studies in
information systems. MIS
quarterly, pp.67-93.

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•Principle of Suspicion
•“Requires sensitivity to possible "biases" and systematic "distortions"in the narratives
collected from the participants.”
•“Either explicit or implicit in critical work is a goal to demonstrate and critique forms of
domination, asymmetry, and distorted communication through showing how social
constructions of reality can favor certain interests and alternative constructions can be
obscured and misrecognized”
Klein, H.K. and Myers, M.D.,
1999. A set of principles for
conducting and evaluating
interpretive field studies in
information systems. MIS
quarterly, pp.67-93.
Hermeneutics –principles of text
analysis

31. © KEM Krauss
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•Autonomisationhighlights a difference between written text and verbal
speech.
•Distanciationmeans that there is a distance in time and space between the
original author and the text. “Since text takes on a life of its own, it becomes
dissociated from the original author, the originally intended audience, and
even its original meaning”(Myers, 2009: 188) which has implications for
reconstructing meaning. A solution could be that one could return to the
original author of the speech to find out what he or she was thinking at the
time (Myers, 2009).
Klein, H.K. and Myers, M.D., 1999. A
set of principles for conducting and
evaluating interpretive field studies in
information systems. MIS quarterly,
pp.67-93.
Hermeneutics –principles of
text analysis

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AI will distance you from the original text

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AI ethics in
proofreading

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How AI can
dilute the
argument or
misconstrue
writing.
ChatGPT 3.5

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ChatGPT 4

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ChatGPT 4

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ChatGPT trains on our data …
even if you ask it to proofread?!?
•https://community.openai.com/t/how-to-use-chatgpt-without-providing-it-
with-training-data/111514/2
•https://www.groovypost.com/howto/opt-out-your-data-on-chatgpt/

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Articulate your use of AI
as part of the scientific
argument

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Policies on AI authorship
•Committee on Publication Ethics (COPE):
•https://publicationethics.org/cope-position-statements/ai-author

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•International Committee of Medical Journal Editors (ICMJE) -authorship
criteria:
•https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-
role-of-authors-and-contributors.html

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May we have some examples and best
practices please?

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My concerns/risks
•AI lacks systematic rigour which only traditional methods can give you
•Author and reader
•Cannot trace argumentation
•Cannot trace alignment, e.g., between literature themes and research questions
•As author I have distanced myself from the original text
•I cannot provethat the summaries are correct
•Incorrect or shallow summaries of papers
•I would not know that the best quotes or summaries have been extracted from the papers
•I have to be the human actor and guardrail
•Nothing has been internalised
•No inference to a better explanation –only existing explanations

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Directions & Reflections
•
Always experiment –start with a field about which you are
passionate or well-informed
•
AI vs. Traditional approaches -not competitive, but complementary
•
AI bias, worldview bias, publisher bias, political bias, first language
English bias, etc.
•
AI magnifies and perpetuates existing biases
•
Use a combination of tools for different phases of the process
•
AI is a moving target
•
Keeping up, experimenting
•
Avoid predatory AI tools/scams
•
Scientific argumentation skills –a required competency

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Thank you
Connect with me:
•https://www.linkedin.com/in/kirstin-krauss
•https://academy.wwis.co.za/

45. Deciding on authorship
Thywill Dzogbewu
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024

46. What is authorship
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Anyone who makes substantial intellectual contributions
to a publication

47. Why is academic authorship
an issue?
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
It is the currency for getting recognition in academia
Authorship determines scientific credit
Promotion
Your academic value
Reputation
The kind collaborators you can attract
The kind of research grants
The visibility of your research centre

48. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Criteria for deciding authorship
Substantial contribution
What is a substantial contribution ?

49. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Order of Authorship
Lead author
Co-authors

Corresponding author

50. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Order of Authorship
What of equal contributions ?

51. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Co-authorship
The order of authors can change at any stage of the reviewing process
Rejected or Major revision required

52. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Why the increase in authorship
challenges
Honorary authorship / Guest authorship
Generosity or sharing
Ghost/anonymous authorship
Mutual authorship

53. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Clarify who will be co-author/s and on what basis
Clarify what is important for each co-author
Recommendations
Clarify the author order at an early stage
If problems arise, talk about them immediately
It is more important to do things right than to do them
quickly
Know the norms and culture that governs the authorship of your
research cluster

54. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
The take home words
Authorship is more than an article – It is how you treat people
Authorship is part of everyday challenge in academia, you need to select the best
approach to handle each situation
Difficult even with guidelines
There is no bulletproof solution
Boundaries governing authorship are unclear, you need to figure it out.
The is a big gap between the ideal world and reality when it comes to deciding who
should receive an authorship credit

55. @SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Thank you

56. Curating your data
Lindah Muzangwa
[email protected]
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024

57. Presentation outline
Introductions-definitions
Why curate data?
Data curation lifecycle
Ethical and legal considerations
Conclusion

58. Introduction-definitions…
What is data?
“…any information that can be stored in digital form,
including text, numbers, images, videos, audios, software
scripts, algorithms, equations, animations, models
simulation, charts, tabular etc”(Johnson, L.R., et al., 2018)
…sets of structured values that can be organized, analyzed
and manipulated by a software application or some other
means of calculation.
A defining characteristic of data is that is machine-readable
This includes data collected directly though digital
manifestations of literature, laboratory data, surveys,
observational data or generated or compiled by machines or
humans surveys

59. Introduction-definitions…
What is open data?
Open data is data that is openly accessible, exploitable,
editable and shared by anyone for any purpose.
Based on the idea that data is more valuable is more
people can use it, and that technology has made the
cost of sharing data negligible
Data Knowledge Value

60. What is data curation?
“..the active and ongoing
management of data
throughout its entire lifecycle
of interest and usefulness to
scholarship” (Cragin et al.,2007)
 Active and ongoing-dynamic
Maintaining and adding value
Lifecycle involves creation,
management and destruction
Aims to maintain the utility of the
data

61. Why curate data?
Good data is
the basis for
good science
The opportunity
cost of losing
data is high

62. Why curate data?
Makes data findable and accessible
Ensures data traceability, quality and integrity-accuracy,
consistency, and reliability of data
Facilitates data sharing and reuse-potential for creating new
knowledge from existing data through re-use, re-analysis,
data mining, innovating combination of data sets
Supports long-term preservation-data is expensive to
generate and some may be impossible to recreate once lost
It is increasingly a requirement of some research funding
bodies
Institutional asset management
Promoting the institution research group or individual-
impact, visibility

63. Data curation-What is your role as
a scientist?
Initial creation and use of data
Managing data for life of project
Using appropriate standards
(where possible)
Complying with data policies
Making the data available in a
format that can (easily?) be used by
others

64. What are the key steps in data
curation?
1. Data collection
•Standard methods
•Ethics
2. Data cleaning
•Missing values, errors
•Statistical software
3.Data
documentation
•Meta data, code books
•Standards
4. Data Storage
Data repositories
Visibility
5. Data sharing

66. Ethical and legal consideration
Ethical issues-privacy, consent, data sensitivity
Legal issues-intellectual property (ownership and
rights), compliance with data protection laws (e.g.,
GDPR)

67. Typical examples of curated data
Manuscripts
Traditional publication-presentational version of the data -
often lacking in supplementals files
Data Journals
Publication option for datasets (discipline specific and peer
reviewed)
Provide useable data releases or independently citable
version of supplemental files
Data repositories
Where data is stored for a long term
Computer accessible
Maybe discipline specific
Can be build for organisations-(universities, funder NGOs etc)

69. Conclusion
Actively curated data will:
Remain technologically accessible
Be easier to understand (therefore use)
If data is made open- widely used - better understood
than isolated data

70. Thank you
www.linkedin.com/in/lindah-muzangwa-4a4345134

71. Responding to reviewers’ feedback
Taahira Goga
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024
University of Cape Town (UCT) |Council for Scientific and Industrial Research (CSIR)
SAJS Outstanding Article Awardee 2023

72. Writing with
integrity
5 June 2024
Introduction
Peer review is a necessary stage in the publication
process.
However, it is a potentially challenging step as it can
be viewed as a stressful experience or a personal
attack.
Many researchers still feel a sense of apprehension
due to the seemingly daunting nature of the process.

73. Writing with
integrity
5 June 2024
Change in Perspective
Reviewers (often scholars with valuable expertise on
your subject matter) are voluntarily giving their time
to ensure the validity of results and provide
feedback.
The intention is to produce an improved version of
the manuscript (more clear, accessible).

74. Writing with
integrity
5 June 2024
Stages in the Article
Submission Process
Stage 1
•Draft article using instructions and guidelines
•Submit article
Stage 2
•Editor decision and receipt of reviewers comments
•Address comments, revise manuscript, and resubmit
Stage 3
•Additional round of review (subject to recommendation)
•Acceptance and publication

75. Writing with
integrity
5 June 2024
Initial Communication
Receive an email from the journal with the editorial
decision, peer reviewer comments, and additional
steps

76. Writing with
integrity
5 June 2024
Reviewer Feedback
Remarks:
•Should feature somewhere before or after the individual comments
• May not be explicitly stated
•Rare for manuscript to be accepted upon first submission

77. Writing with
integrity
5 June 2024
Examples of Reviewers’
Comments
General Criteria:
-Novelty
-Abstract/Problem
-Methods
-Quality/Style
-References
-Compliance with
journal scope
-Contribution to field

78. Writing with
integrity
5 June 2024
Examples of Reviewers’
Comments

79. Writing with
integrity
5 June 2024
Examples of
Reviewers’ Comments
(Same Manuscript)
Remarks:
•Some similarities but also contrasting views

80. Writing with
integrity
5 June 2024
Suggested Approach
•Download all comments
•Initial screening of feedback
•Ensure that the response is self-contained
•Respond to every point raised by the respective
reviewers
•Try and respond directly to the comments and
explain the changes made
•Emphasise changes made for easier navigation (e.g.
track changes, colour and font variation)

81. Writing with
integrity
5 June 2024
Additional Aspects
•Be respectful of reviewers – even critical comments can
provide food for thought
•Accept any faults and acknowledge comments and
suggestions for improvement
• If a reviewer sees a problem or fails to understand
something, it may be due to the contents of the
manuscript, which may hamper the readers’
understanding

82. Writing with
integrity
5 June 2024
Additional Aspects
•Indicate section, page, line number for easier
navigation
•Comments from reviewers who are not experts in
your field are particularly useful – produce a paper
that is more accessible to the general, non-
technical audience
•Take the review process in your stride and strive to
respond in a professional manner – take time to
digest the comments and have a fresh look.

83. Writing with
integrity
5 June 2024
Response to Reviewers
Included Aspects (pt 1)
-Addressed/noted
-Explanation
-Location in text
-Additional
reasoning

84. Writing with
integrity
5 June 2024
Preparation for
Resubmission
•If you are the lead author, distribute your responses
amongst your co-authors together with a deadline
for feedback
• If necessary, have a team meeting to discuss the
trickier comments
• Assess and integrate the comments and create a
final version of both documents
•Upload and resubmit according to the instructions
•May be multiple rounds

85. Writing with
integrity
5 June 2024
Final Remarks
Part of becoming a successful and emotionally stable
author is learning to navigate the peer review
process.
It requires perseverance and patience but the
process will hopefully improve your manuscript and
aid in emphasising the value of your research.

Best of luck!

86. Writing with
integrity
5 June 2024
References/Sources
•Muchenje, Voster. (2017). Editorial: How to
respond to reviewers' comments.South African
Journal of Animal Science,47(2), 116-
117.https://dx.doi.org/10.4314/sajas.v47i2.1
•Editage. A template for responding to peer reviewer
comments (editage.com)
•Taylor and Francis Author Services. How to
respond to reviewer comments - Author Services
(taylorandfrancis.com)
•Scientific Writing with Karen L. McKee
•Navigating Academia

87. Responding to reviewers’ feedback
Taahira Goga
www.linkedin.com/in/taahiragoga
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024
University of Cape Town (UCT) |Council for Scientific and Industrial Research (CSIR)
SAJS Outstanding Article Awardee 2023

88. Communicating
with editors
Leslie Swartz,
Editor-in-Chief, SAJS
@SAJS_Official @ASSAf_Official
#AcademicWriting #ResearchIntegrity
Writing with integrity
5 June 2024

89. What editors want
•Good quality, interesting submissions
•That fit the scope, mission and vision of the journal
•Excellent, supportive and informed peer review
processes
•Quick turnaround
•Maximum impact
•To be at the cutting edge where possible
•Good relationships between editors, authors,
reviewers

90. What authors want
•Good quality, interesting submissions
•That fit the scope, mission and vision of the journal
•Excellent, supportive and informed peer review
processes
•Quick turnaround
•Maximum impact
•To be at the cutting edge where possible
•Good relationships between editors, authors,
reviewers:
•In other words: WE ARE ON THE SAME SIDE

91. Who are the editors of journals?
•Mainly: academics just like you
•Generally speaking, doing editorial work for no
remuneration (sometimes there are small fees given,
but this is the exception)
•They have day jobs
•On larger journals like SAJS there is an editorial team
running the journal on a day to day basis
•Doing the work for interest and enjoyment (and it is
fun!) but also as a form of academic service and care
•We are not the enemy or the punishing parent

92. What do editors think of you, as
an author?
•Editors usually don’t know and have no opinion
about you (sorry)
•They are much more concerned about the
academic conversation in their journal and whether
and in what way your work contributes
•Editors are grateful when you are polite and follow
the steps (from submission and throughout)
•Editors, like all of us, are far more likely to be
positively disposed to well- prepared, polite authors

95. Please read
the journal
policies – it
takes us a
long time to
write them!

96. Helping the review process along
•If a journal asks for/allows this, always suggest
reviewers. These must have no affiliation to you.
•You may suggest reviewers to exclude
•The editorial team does not have to take your
suggestions on
•If you have heard nothing for three months
(Minimum!) write a polite letter to the editor and
ask if you can help by, for example, suggesting
more reviewers

97. You have a right
to complain, but
do this collegially
•“I need this for my PhD” is not
a basis for a complaint
•Neither are:
•But I think this should be
in your journal scope
•But I graduated cum laude
with this work
•My funder needs me to
publish this
•Your journal is
incompetent
•Support good things
done by the journal
•Generally, unless you
have a strong case, just
submit elsewhere

98. Some don’ts in terms of dealing with editors
DON’T TELL THE EDITORS
HOW LUCKY THEY ARE TO
HAVE YOUR WORK
DON’T THREATEN THE
EDITORS WITH
REPUTATIONAL DAMAGE
OR OTHER THINGS
DON’T OFFER
MONEY/BRIBES (THIS IS
FOR PREDATORY
JOURNALS)
IF YOU WANT TO
WITHDRAW A PAPER, THAT
IS YOUR RIGHT – DON’T
USE IT AS A THREAT

99. Some do’s
Follow all the instructions
Format according to what the journal wants – even if you don’t like the format (except
your paper your way submissions)
Please proofread
Please check for inadvertent plagiarism
A good, clear covering letter can help
Be friendly, say thank you – even when you get a rejection (as I did this morning!)