Peer Review in Education

Your competence at work is judged in seconds. Even when you over-deliver, you can be underestimated. Every day, false assumptions about you are made: — Polite = Weak — Older = Not agile — A foreign accent = Less capable — Introverted =  Not a strong leader — Woman =  Softer voice, less authority It's not just unfair. It's exhausting. So the question is: How do you beat biases without changing who you are? Here’s what I recommend: 𝟭. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝘁𝗵𝗲 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 → Speak about impact, not effort. → Articulate your value proposition. →“Here’s the problems I solve. Here's how. Here’s the result."  If no one knows what you bring to the table, they won’t invite you to it. 𝟮. 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿 Silent excellence is wasted potential. → Speak up when it feels risky. → Build real not just strategic relationships. → Share insights where people are paying attention. You don’t need to be loud. You need to be seen. 𝟯. 𝗧𝘂𝗿𝗻 𝘆𝗼𝘂𝗿 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗶𝗻𝘁𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗼𝗿𝘀 The traits that trigger assumptions? Those are your edge. → Introverted? That’s deep listening. → Accent? That’s global perspective. Don’t flatten yourself to fit. Distinguish yourself to lead. 𝟰. 𝗢𝘄𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗲𝘀𝗲𝗻𝗰𝗲 → Say “I recommend” not "I think.” → Hold eye contact. Take up space. → Act like your presence belongs (even when others haven’t caught up.) Confidence isn’t volume. It’s grounding. Bias is everywhere. But perception can be changed. Don't let other people's false assumptions define you. Do you agree? ➕ Follow Deena Priest for strategic career insights. 📌Join my newsletter to build a career grounded in progress, peace and pay.

We all want to reward employees fairly, yet decades of research--and for many people, their lived experience--show that bias persists. In other words, for the same performance, people earn less or more due to managerial error. New research from researchers at our Stanford VMware Women's Leadership Innovation Lab shows that many interventions are only targeting half the problem. Bias shows up both in how managers describe (view) performance as well as how they reward (value) behaviors. Viewing biases often show up in how performance is described differently based on who is performing it. Men’s approach may be called “too soft,” thus “subtly faulting them for falling short of assertive masculine ideals.” Valuing biases can show up as the same behavior being rewarded when men perform it but not when women do. Examples from the research show that men benefitted when their project specifics were described, whereas women were not. So the same description and behaviors showed up in reviews, but they were only rewarded on men’s. What can be done to curb biases? ✅ Standardize specific guidelines for how managers should view employee behaviors and assign corresponding rewards when giving employees feedback and making decisions about their careers. ✅ Help managers catch bias in both viewing and valuing. ✅ Monitor these impacts from entry level to executive leadership. It turns out that as the criteria shift, so can the way these biases work. A key lesson from our research shows that the work takes discipline, consistency and accountability. These steps may seem like a lot of “extra” work, but at the end of the day, managers also benefit when they weed out biases and fairly promote the most talented employees. Article by Alison Wynn, Emily Carian, Sofia Kennedy and JoAnne Wehner, PhD published in Harvard Business Review. #diversityequityinclusion #performanceevaluation #managerialskills

Recently, a fellow DEI practitioner reached out to me for advice. She was preparing for her organisation's upcoming talent review cycle and looking for a short #UnconsciousBias e-training that managers could complete before assessing employees' potential. The goal was a good one: make talent decisions more objective and fair. My response went another way, though: I didn't recommend a training. I asked whether they should be assessing "potential" at all. I know this might sound odd coming from someone who works in #diversity and #inclusion. After all, I've spent years trying to reduce #bias in talent decisions through training, calibration meetings and awareness campaigns. But lately I find myself asking a different question: 🤔 What if the problem isn't that we're assessing potential poorly, but that we're assessing it in the first place? Earlier this week, while clearing out my inbox, I came across an article my manager Courtney Bass Sherizen had sent me: "The Leadership Divide" by Hogan Assessments – and it put words to something I've been thinking about for a while. The article highlights a frustrating reality: the qualities that help people get promoted are often not the qualities that make them effective leaders. ↠ Confidence gets mistaken for competence. ↠ Charisma for capability. ↠ Visibility for value. And because "potential" is often loosely defined, we end up relying on subjective impressions about who looks like a future leader. And that's where bias thrives: because concepts like "executive presence", "leadership potential" and "readiness" are often vague enough for #stereotypes to fill the gaps. Look around your organisation: Who seems confident? Who seems strategic? Who feels like leadership material? We rarely ask whether we're evaluating demonstrated capability or simply rewarding familiarity. This is one reason I increasingly prefer anchoring talent discussions in evidence of #performance rather than predictions of future greatness. While performance reviews are far from perfect, at least they assess proven behaviour: unquestionably easier to examine, challenge and calibrate than speculation about the future. A few practical questions to review talent processes: 1️⃣ Can every criterion be observed and evidenced? 2️⃣ Are we rewarding demonstrated behaviours or perceived future promise? 3️⃣ Could two leaders define "potential" in the same way? 4️⃣ What decisions would change if performance carried more weight than prediction? 5️⃣ Are we promoting people because they are effective, or because they fit our mental image of a leader? Unconscious bias training does have its place, but sometimes the most effective way to reduce bias isn't helping people make subjective decisions better, but instead reducing how many subjective decisions we ask them to make in the first place. 💬 Does your organisation assess potential? How do you make the assessment as objective as possible? 🔗 References in comments.

What do reviewers notice that authors miss? During my recent experience as an external reviewer for two different international publishers: -Elsevier  -Virtus Interpress I reviewed two academic papers published in: -Journal of Accounting Education -Journal of Governance and Regulation Despite the differences in journals and contexts, the review comments revolved around almost the same core themes, Points I am sharing here with any researcher aiming to publish in a high-ranked journal: 1️⃣ The title is not a marketing façade The title must accurately reflect the core substance of the research. In one of the papers, the work was rich and important, yet the title was misaligned with the content, this is a fundamental concern for any reviewer, regardless of the paper’s overall quality. 2️⃣ The research gap… or nothing Without a clear research gap, there is no real scientific contribution. A gap is not a rephrasing of what already exists, but a logical justification of what has not yet been addressed. 3️⃣ Methodology is not a formal procedure Methodology must be: -Aligned with the research question -Justifiable -Replicable 4️⃣ Statistical analysis: quality over quantity The issue is not “how many statistical tests were used,” but rather: -Is the analysis robust?  -Has it undergone sensitivity testing? In my review, I focused particularly on: Sensitivity analysis The quality of results, not merely their statistical significance This aligns with what I teach and deliver in Systematic Review & Meta-Analysis courses, where we use critical appraisal tools such as JBI to assess methodological quality not just form. 5️⃣ References are not academic decoration Do not include references that are not actually used in the paper. A reviewer immediately notices an inflated reference list with no analytical function. 6️⃣ Artificial intelligence: an enhancement tool, not a substitute for the researcher I explicitly stated to the publisher that AI was used only to improve academic writing quality. AI can: -Improve phrasing -Enhance clarity But it is not the author, nor the source of ideas or methodology. Conclusion Peer review is not about fault-finding; it is a test of the quality of research thinking, from the title to the final reference. If you are a researcher, always ask yourself: Would my paper convince an editor… before it convinces me?

Just wrapped up our inaugural prof-review session in the community - remember, most papers don’t get rejected because of poor results. They get rejected because editors can’t see the value of those results fast enough. I was reminded of this in a live peer review session I ran earlier today. Here’s what kept coming up. 1. Weak abstracts kill good papers Make sure to include: - Big picture problem - Specific gap in the literature - What you did (1–2 lines max) - Key results (only the headline numbers) - Why it matters If an editor can’t answer “so what?” from your abstract alone, you’ve lost them. 2. Literature review ≠ research gap Listing prior work is not enough. You need a bridge: - What has already been done - Where those studies fall short - How your paper removes (part of) that limitation Write the gap as if the editor will only read those 3 sentences. Because often, that’s exactly what happens. 3. Results are described, not discussed Common pattern: “X increased, Y decreased, Z was highest at 30%.” What’s missing: - Is this aligned with previous studies, or not? - By how much do you differ (relative error, percentage points)? - What insight does this unlock for future work or practice? Data without context feels like a lab report, not a journal article. 4. Structure quietly signals quality The small things matter more than most people think: - Avoid one-paragraph subsections – group results by themes - Keep figures and tables consistent (Fig. 4a/4b, not “left/right”) - Use an equation editor and a clear nomenclature table - Always close with limitations and future work This is the difference between “good student work” and “publishable research”. I’ll run weekly or biweekly live peer-review sessions so members can address these issues before submission, not after rejection. Would you like your manuscript to be considered for a future live review? Join my free community! #science #scientist #research #researcher #publishing #peerreview #phd #postgraduate #professor #academic #academia

Most papers get rejected But it’s rarely the writing. I review a lot of papers. Most are technically fine. Some are well written. And yet, they still get rejected. Here are the real reasons this happens. 1. The paper never answers “why this matters.” Not to you. Not to the field. Not beyond a narrow academic loop. If the contribution is not clear reviewers disengage early. 2. The research question is safe, not necessary. If the paper feels like it exists because the data were available, not because the question demanded asking, reviewers sense that immediately. 3. The literature review describes, but does not position. Summarising prior work is not the same as showing where you stand. If the reader cannot see what gap you are stepping into and why you are the right person to do so, the paper feels directionless. 4. The methods are correct, but disconnected. Good methods do not rescue weak alignment. If the design does not clearly serve the question, reviewers see the study as technically competent but conceptually thin. 5. The discussion repeats results instead of thinking with them. Many papers stop too early. A discussion section should interpret, extend, and challenge. If it only restates findings in softer language, the paper feels unfinished. 6. The paper tries to please everyone. Broad claims with cautious conclusions often signal a lack of confidence. Strong papers take a position, even if it invites disagreement. 7. The journal was never the right home. Rejection is often about fit, not quality. A solid paper sent to the wrong audience will struggle regardless of merit. Rejection is not a sign you cannot write. It is usually a signal that the thinking has not fully crystallised yet. Writing is execution. Judgement is the hard part. And that is the part reviewers are responding to.

Who you “click” with can shape your career more than you think. Decisions about promotions, projects, and visibility aren’t just about skill, they’re often influenced by how familiar, friendly, or likeable someone feels. Bias in action isn’t always obvious: • We tend to gravitate toward people who share our background, hobbies, or communication style. • Preferences for “fit” can mask unfair exclusion. • Teams miss out on talent when likeability outweighs ability. The solution? Be intentional about who gets opportunities, feedback, and visibility. Ask yourself if comfort is guiding your decisions and flip the script. Here are 10 ways to spot and counter affinity bias: 1. “I just get along with them better” → “Who else could bring skills we’re overlooking?” 2. “They have the same style I do” → “What unique perspectives could a different style contribute?” 3. “I enjoy their sense of humor” → “Does this person’s work demonstrate capability and results?” 4. “They remind me of someone I like” → “Am I favoring familiarity over talent?” 5. “I’d have coffee with them” → “Would I trust them with responsibility or leadership?” 6. “We share the same hobbies” → “How are they showing expertise and problem-solving?” 7. “They speak the way I do” → “Am I valuing skill and insight above tone or accent?” 8. “They fit in socially” → “Do they bring ideas or results that benefit the team?” 9. “I can picture them on my team” → “Do they have the right experience or skills for the role?” 10. “They just feel likeable” → “Am I giving others an equal chance to shine?” Likeability bias isn’t about being unfriendly (or hiring people that are), it’s about making sure comfort doesn’t replace competence. 💬 What strategies have you used to catch yourself leaning on “fit” over skill? 🚀Want to go deeper? Hustle Crew workshops give teams practical tools to spot bias, challenge assumptions, and build workplaces where everyone can thrive. ♻️ Share this to help others create fairer, stronger teams.

In a new paper at Organization Science, we find that gendered responses to expressions of passion—a commonly used criterion in evaluating potential—both penalize women and advantages (unexceptional) men in high-potential selection processes (joint work with Joyce He and Celia Moore). https://lnkd.in/eeBjc_7j Across two studies—an actual talent review process and a preregistered experiment using videos with trained actors (plus two supplementary studies)—our paper shows: 1️⃣ Replicating prior work, we find a gender gap in high potential designations: men are more likely than women to be designated as high potential even when they perform the same. 2️⃣ Gender biases around passion provide one helpful insight into why this difference occurs. We find: ➡️ a male advantage: passion more meaningfully shifts predictions of diligence for men than women ➡️ a female advantage: passion is viewed as less appropriate for women than men, in particular those expressions which are highly affective and likely evoke stereotypes of women as "overly emotional" We summarize our work in a new Harvard Business Review article, including recommendations for what organizations can do to fix the gendered passion bias: https://lnkd.in/eT7DAdsq 1. Prioritize clear and objective criteria. Where possible, focus on concrete and objective indicators to evaluate potential rather than using subjective criteria like passion. 2. Encourage direct conversations over emotional displays. Rather than inferring how passionate and hardworking an employee appears to be based on their emotional expressions, managers should engage in meaningful conversations with employees to thoroughly gauge their commitment and motivations. 3. Broaden the criteria for high-potential selection. Expand the criteria for evaluating potential to include a mix of personal values, goals, and skill sets, which can help provide a fuller picture of an employee’s qualifications. 4. Conduct regular bias audits. Implement regular assessments of high-potential programs to identify gender or other biases in their selection process. 5. Consider raising the bar for moderately performing men. Given that reasonably high-performing men often receive an added boost from expressing passion, consider raising the performance bar for this group — for instance, by expecting higher levels of diligence commensurate with expectations for women.

If your organisation is serious about reducing bias, stop starting with the individual. Start with the environment. Here's why. The context you put people in shapes their automatic thinking far more than any personal attitude they hold. Segregated networks, unequal pay structures, homogeneous leadership teams - these things activate biased thinking in basically everyone, regardless of their values or intentions. Change the context, and you change behaviour at scale. Try to change the individual, and you're fighting an uphill battle with weak results. This is the practical implication sitting underneath some genuinely important new research. Big thanks to B. Keith Payne for this thorough review of where the implicit bias field actually stands right now. The headline finding most people miss: individual implicit bias scores are pretty rubbish predictors of actual behaviour. Your IAT score on a Wednesday tells you almost nothing about how you'll act on Thursday. Critics used this to dismiss the whole field. But zoom out to city or county level, and implicit bias scores become remarkably stable and strongly linked to real-world gaps in policing, healthcare, and school discipline. So practically speaking: Stop over-investing in individual bias training. The evidence that it changes behaviour in any meaningful way is genuinely weak. Audit your structures instead. Who actually gets promoted, hired, or disciplined? Patterns in outcomes across teams and departments are far more diagnostic than any assessment tool. Design contexts that reduce the cues that trigger bias in the first place. Structured interviews, blind review processes, diverse hiring panels - these work because they change the environment, not the person. The science has moved from blaming individuals to understanding systems. Your DEI strategy should have made the same move by now.

Reviewer comments: What they really mean… Academic reviewers sometimes speak in code. Here's some possible translations: "This lacks novelty" Translation: Your contribution isn't clear enough. They can't see what's new because you haven't spelled it out. Fix your abstract and conclusion first. "The methodology is questionable" Translation- Usually a methods section problem, not a fundamental flaw. Add more detail, justify your choices, acknowledge limitations upfront. "The writing needs improvement" Structure and flow issues, not your intelligence. Your argument probably jumps around. Create better signposts and transitions. "This would benefit from a broader literature review" Translation: You've missed something obvious (to them). Or you haven't connected your work clearly to existing debates. "The implications are unclear" Translation: You've buried the lead. Move your key insights up front and make them impossible to miss. "The sample size is too small" Translation: Either justify why it's appropriate for your research question, or acknowledge it as a limitation and explain next steps. Or your sample size really is too small. "This is more suited to a specialist journal" Translation: Your work is good but too niche for this audience. Don't take it personally, just aim more specifically. "The theoretical framework needs strengthening" Translation: You're applying theory without really engaging with it. Show how your work extends, challenges, or refines existing theory. "The conclusion overstates the findings" Translation: Dial back the big claims. Be precise about what your data actually shows versus what it suggests. The pattern? Most "brutal" reviews are actually pointing to fixable communication problems, not fundamental research flaws. Reviews are rarely personal attacks. Start reading them as editing notes from people who want your work to be better.