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Enregistrement W4311058198 · doi:10.1002/fee.2575

Action to support early career ecologists

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

Notice bibliographique

RevueFrontiers in Ecology and the Environment · 2022
Typereview
Langueen
DomaineEnvironmental Science
ThématiqueConservation, Ecology, Wildlife Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMentorshipEquity (law)Inclusion (mineral)Public relationsDisadvantagePolitical scienceGrantsmanshipCall to actionPsychologyBusinessHigher educationMarketingSocial psychology

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,760
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,276
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2022
Routes d'admission1
Résumé présentoui

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