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Record W4311058198 · doi:10.1002/fee.2575

Action to support early career ecologists

2022· review· en· W4311058198 on OpenAlexaboutno aff
Kathleen A. Carroll, Pacifica Sommers, Cari Ficken, Angela Doerr, Nathan Emery, Matthew E Aeillo‐Lammens, Sarah R. Supp

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicConservation, Ecology, Wildlife Education
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipEquity (law)Inclusion (mineral)Public relationsDisadvantagePolitical scienceGrantsmanshipCall to actionPsychologyBusinessHigher educationMarketingSocial psychology

Abstract

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The Early Career Ecologist (ECE) section of the Ecological Society of America (ESA) emerged in 2014 to organize and centralize support for ECEs struggling with unique challenges, particularly involving career transition. The ECE section has since grown to over 500 members and participates in ESA leadership and programming. In this editorial, as current and former chairs of ESA's ECE section, we define “early career” individuals as having fewer than eight years of full-time employment or those who are in a job transition phase, and we identify four areas where individuals and organizations can better support ECEs: namely, (1) equity and inclusion; (2) funding; (3) mentorship and training; and (4) career diversity. Improving equity and inclusion to expand opportunities for ECEs requires targeted actions, including broadening success criteria, providing clearly defined leadership opportunities through equitable recruitment processes, using inclusive language, offering and participating in programs that further equity and inclusion goals, and reducing financial barriers. Historically, academic success has been measured through scholarly publications, citations, and grantsmanship – criteria that disproportionately disadvantage ECEs, particularly those from historically marginalized groups or working outside academia. Progress comes in many forms, and we believe success criteria should be broadened in decisions related to hiring, promotions, and awards. Even small actions of inclusion (for instance, including pronouns in communications) can have large positive impacts (such as signaling a welcoming atmosphere). We also recommend that established ecologists in decision-making positions (hereafter, “later-career” individuals) seek opportunities to increase their understanding of equity and inclusion, and that ESA facilitate these efforts. There are ample resources available covering topics such as implicit bias, microaggressions, and other common concerns. Barriers to identifying and obtaining funding represent one of the biggest challenges ECEs face. ECEs, especially in academia, are often poorly compensated. Changes to salaries and power structures are needed to improve ECE compensation and should be amplified by later-career ecologists, especially those in administrative or budgetary leadership roles. ECEs in career transitions or short-term positions may be uncertain about what is acceptable to ask or advocate for, including funding for travel or professional development, and – without adequate mentoring – may unintentionally self-limit their opportunities. This may be especially true for first-generation college graduates who have less formal experience with career norms in scientific fields, further exacerbating issues of equity and inclusion. Mentors can proactively suggest opportunities and clarify what funding is available for their mentees. Societies like ESA as well as academic institutions should standardize and publicize guidelines for funding opportunities and facilitate funding-related assistance for ECEs. Many ECEs, especially those seeking careers outside of academia, have noted a deficit in mentorship with respect to professional development during and after graduate school. We recommend that academic institutions and professional societies prioritize active, intentional mentorship for ECEs, and establish training, guidance, and expectations for both ECEs and mentors, including systems to promote accountability. Later-career individuals should seek out mentoring opportunities and volunteer to mentor through existing programs (for example, EcologyPlus [https://esa.org/ecologyplus]) or events (including ESA annual meetings). Within ESA, ECEs consistently express the need for resources that explore a variety of careers, particularly those outside traditional academic settings. At the 2022 ESA annual meeting in Montreal, Canada, the most popular suggestion among ECE members was to increase exposure to diverse careers. For the 2023 annual meeting in Portland, Oregon, ESA chose “For all ecologists” as its theme, and we look forward to that event's envisioned expanded programming for non-academic ecologists, especially ECEs. Such an agenda is a good first step in providing ECEs with meaningful exposure to a broad range of career opportunities and enabling ecologists outside of academia to explore the benefits of long-term membership within a professional society. We further recommend that such societies cosponsor sessions with applied industry sector groups and reach out to a variety of employers to host job panels or networking events. ESA's ECE section continues to provide many resources to support its members, including webinars, workshops, and mentorship programs (www.esa.org/earlycareer). We are optimistic that these tools will encourage individuals, institutions, and other professional societies to develop new opportunities to support ECEs. We also hope that the field can better retain a diverse assembly of professionals through this challenging transitional career period. This will require investment from all levels, and so we ask all ecologists to reflect on how they currently (or could better) support ecology's next generation. While acknowledging that our recommendations may not reflect the experiences of all ECEs, we aspire to incorporate broad and actionable insights for those creating and improving supportive spaces for ECEs across a spectrum of lived experiences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.042
GPT teacher head0.276
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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