The <scp>ESA</scp> Editorial Fellowship: Navigating the Publishing Landscape as Early‐Career Scientists From the Global South
Bibliographic record
Abstract
Publishing is a cornerstone of scientific development and progression of scientific careers, often serving as a currency or benchmark guiding decision-making for academic jobs, grant applications, awards, and more. Maintaining the publishing system is complex. It relies on the quality of submitted manuscripts but also on the collaborative efforts of editors and reviewers. Authors write and submit manuscripts, often without being fully aware of the criteria that will be used by editors to assess the fit of the manuscript for a particular journal. Editors ensure that manuscripts align with the journal's scope, rigorously evaluating their scientific relevance and managing the peer-review process. Then, volunteer reviewers assess the soundness, originality, and coherence of the research, offering constructive feedback to refine the work. Despite its central importance to scientific careers, there is a visible gap in specific training for academic publishing, with journals usually lacking training spaces and materials for all the actors involved in the publishing ecosystem (but see British Ecological Society 2013). In our experience, while you are trained in academic writing during a PhD, graduate programs often fail to provide training for students in the other activities of academic publishing, instead relying on individual advisors for this experience. Hence, graduate students and early-career researchers are expected to publish, often in well-known outlets and contribute time to the publishing ecosystem (reviewing, even editing), yet there are few formal opportunities to prepare them for this critical part of their career. In addition to the lack of training, the fast-paced shifts in academic publishing pose additional challenges for researchers to effectively navigate the publishing landscape without proper training. These changes include an exponential growth of the “pay to publish” system for open access to articles (free to read), AI tools, adoption of double-anonymous review systems, and open science practices. While providing new opportunities for researchers in many instances, these changes require researchers to constantly learn and adapt to this quickly evolving publishing ecosystem (Box 1). Open publishing agreements now allow some researchers to publish “for free” and other agreements provide discounted rates in specific journals, but many researchers are unaware of these opportunities. AI tools that can generate codes for statistical analyses or even text (e.g., ChatGPT) introduce new ethical challenges, which are being addressed by new guidelines from societies and publishers on when and how to use such tools (COPE Council 2024). Peer-review models have also shifted, with discussions surrounding review type (e.g., single-anonymous or double-anonymous; Cássia-Silva et al. 2023; Fox et al. 2023) igniting debate on how to balance scientific rigor, fairness, and accountability. Open science practices are encouraging greater transparency by mandating data and code sharing, as well as changing publishing timelines and accessibility by allowing researchers to share early versions of their work in preprint platforms. These changes reflect the increasingly complex and dynamic nature of publishing, underscoring the urgency of specific training opportunities, especially when they are happening in an already complex system characterized by various socioeconomic barriers. Institutional agreements: Open publishing agreements or discount agreements are often established between academic publishers and institutions such as universities or research consortia. Such agreements provide researchers with the opportunity to publish their work in hybrid or gold open-access journals without paying article-processing charges (APCs) directly, which is often done through grants or even personal funds. These agreements, generally referred to as transformative agreements, include several different types of agreements designed to increase accessibility to publishing while transitioning the publishing ecosystem toward openness. These agreements are important as they reduce financial barriers for publishing open access, enabling researchers to reach wider audiences and tackling inequalities in funding distribution. However, understanding the agreements each case can fall within is hard, since eligibility criteria and benefits might vary between institutions, publishers, and even individual journals. For this reason, researchers are often unaware of all the available opportunities relating to such agreements. Artificial intelligence: AI-powered tools, such as ChatGPT and other machine learning-based language models are becoming increasingly used in everyday life and have also penetrated academia. Such models can potentially assist researchers by generating text, enhancing clarity and coherence, improving language usage, assisting with data analysis, and streamlining administrative processes. This can be particularly useful for whom English is not their first language. While they offer opportunities to better manage writing and publishing, their use raises ethical challenges given how they are being used by undergraduate and graduate students or by researchers. Publishers are now adapting to these recent innovations and have been recognizing the potential of AI tools to improve academic publishing but also requiring that authors openly state how they were used to ensure transparency and academic integrity (Spanjol and Noble 2023). Peer reviewing: Traditional single-anonymous reviews—where reviewers remain anonymous to authors, but reviewers know author identity— encourage candid assessments of the work in face of potential retaliation. On the other hand, double-anonymous reviews, where both authors and reviewers remain anonymous to each other, go a step further by reducing unconscious biases from the reviewer's side toward any aspect of the author's identity, such as gender, nationality, or perceived native language. Recent experiments by the British Ecological Society have demonstrated that double-anonymous reviews reduce the positive biases that are increasing the scores of manuscripts from Global North authors and native English speakers. Emerging models like triple-anonymous review, which anonymize authors even from editors are a step further considering that editors might also be unconsciously biased (Barros et al. 2021, Cássia-Silva et al. 2023), would make the publishing landscape even more complex. Finally, some people advocate for open reviews, which might encompass both that all the identities are fully disclosed or that, in addition to this, the reviews are also openly accessible to the readership. These options promote transparency and accountability, reducing the risks of unfair or offensive review processes, and might enhance the role of peer reviewing to career development by making the personal contributions open. Open repositories: Journals are increasingly mandating that authors share data and code openly to promote transparency and reproducibility. These mandates are essential for ensuring scientific integrity and robustness since they allow others to replicate analyses and improve their capacity to build upon published studies. However, this requirement brings several challenges. Sensitive data, such as the distribution of endangered species, requires special consideration to avoid further harming them. Similarly, respecting data sovereignty, especially when working with Indigenous Knowledge and Traditional Ecological Knowledge, involves navigating complex cultural, ethical, and legal frameworks. Additionally, open data might pose negative feedback for long-term monitoring or to primary research conducted in underfunded places, since their funding might depend on the novelty of the data and might risk unethical parachute science that can jeopardize a project's stability in the long run. Despite journal mandates, compliance remains inconsistent. Researchers might state that data and code will be available upon request, which are mostly unfulfilled (Tedersoo et al. 2021). Even when data are shared, available data might be incomplete or insufficient, which might be related to the lack of skills to properly organize data and code following good standard practices (Roche et al. 2015). Finally, preprint platforms provide novel spaces in the publishing ecosystem, allowing researchers to share early versions of their work openly, speeding up science dissemination, and reducing barriers to access research behind paywalls (Noble et al. 2025). Barriers in publishing continue to disproportionately affect authors from the Global South (Fontúrbel and Vizentin-Bugoni 2021, Fox et al. 2023). Unequal access to training and information (often only available in English) combined with language barriers and limited research funding can result in biased peer review and lower visibility (Ramírez-Castañeda 2020, Fontúrbel and Vizentin-Bugoni 2021, Fox et al. 2023, Naidu et al. 2024). Differences in writing conventions, such as the overuse of geographic markers, might unintentionally reduce the perceived scope of Global South papers (Nakamura et al. 2023). Furthermore, most high-impact journals are based in the Global North, reinforcing systemic inequities and promoting some ways of knowledge production (Salager-Meyer 2008, Bol et al. 2023) that might privilege temperate-zone ecology over tropical regions (why is ecology from the temperate regions “ecology,” and ecology from the tropical regions “tropical ecology”?; Soares et al. 2023) and foster a narrow perception of global relevance. Although some journals have made efforts to deal with biases such as transitioning to double-anonymous reviewing or open reviews and/or allowing abstracts written in different languages, there is still much to be done (Ramírez-Castañeda 2020, Amano et al. 2023). Society journals are in a strategic position to implement better policies for their journals. For example, they can leverage AI technology to improve text readability and translations, provide space for non-English abstracts (or manuscript translations), encourage multilingual dissemination, and provide mentorship and financial support for improving English skills (Amano et al. 2023, Fair et al. 2024). The Ecological Society of America (ESA) has been pushing forward several actions and programs to improve the publishing ecosystem in biodiversity science. For example, the ESA provides its members one free open access publication per year in one of their hybrid journals, but few members take advantage of this benefit to support the open science mission. The ESA journals also allow joint reviews, enabling researchers to mentor early-career researchers in reviewing scientific articles and collaborate with the publishing landscape through mentoring. ESA also organizes several opportunities for learning academic publishing, including written materials (Harley et al. 2004) and several sessions during the society's annual meeting. Finally, in 2024, ESA launched its first edition of the Editorial Fellowship inviting early-career researchers to join their editorial team for a two-year fellowship for leadership training and giving them/us the possibility to pursue editorial projects. The Editor-in-Chiefs of the ESA journals also provide mentorship for the editorial fellows on the publishing ecosystem and the fellows' projects tackling editorial issues. As ESA Editorial Fellows and Latin American researchers living and working in the Global North, we understand the critical importance of addressing the challenges faced by early-career researchers, especially those situated in the Global South, in navigating the publishing landscape. We recognize how our privileged educational background and lived experiences in Latin America, summed with our work experience in the Global North, might shape our perspectives, pushing us to renegotiate our identities as we move between places, majority vs. minority population, and career stages (Echeverri et al. 2022). Nonetheless, we believe this specific positionality offers a unique opportunity to bridge the gaps between regional scientific communities and a more global science. Over the next 2 years, we aim to address three main areas that tackle the issues we perceive in the publishing landscape. First, we seek to better understand how language biases influence academic publishing and their specific impact on the publications in ESA journals. Second, we expect to provide training opportunities for publishing, with a special emphasis on supporting researchers from the Global South and early-career researchers. Finally, we expect to contribute to improving the shared benefits of open code and data by helping to establish clearer guidelines and good practices that balance transparency with equity and respect for sensitive data. In summary, we expect to meaningfully contribute to the accessibility of academic publishing, tackling long-standing issues. We understand that true equality is a long way ahead and will take generations of ESA scholars to address this. We hope to provide information for ESA to tackle global inequality in academic publishing. For this, we will work to publicize and provide training on some of the ESA programs and tools presented here. We will focus on developing training materials and mentoring targeted toward the Global South and on testing the effect of some of these interventions in making the publishing ecosystem more inclusive. We are thankful to the editors-in-chief of the ESA journals and ESA staff that have been engaged with the Editorial Fellowship program for their support and mentoring. We declare no conflicts of interest. We did not collect any data for this manuscript.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".