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Enregistrement W4367604959 · doi:10.1002/fee.2631

Allyship requires action

2023· review· en· W4367604959 sur OpenAlexaff
Ezra J. Kottler, Kyle M. Shanebeck, Sharon K. Collinge

Notice bibliographique

RevueFrontiers in Ecology and the Environment · 2023
Typereview
Langueen
DomaineDecision Sciences
ThématiqueInterdisciplinary Research and Collaboration
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésTransgenderQueerHarmPovertyMental healthFace (sociological concept)Affirmative actionLesbianAction (physics)Public relationsPolitical scienceCriminologyEmployment discriminationPsychologySociologySocial psychologyGender studiesLawPsychiatry

Résumé

récupéré en direct d'OpenAlex

Queer and especially transgender ecologists face unique barriers in the pursuit of professional training, resources, and career opportunities, and without targeted actions to overcome these barriers, motivations to make our institutions more equitable will not translate into meaningful change. Saying our institutions are safe for and welcoming of LGBTQ+ people is insufficient to achieve the necessary outcomes. Though social progress has made it possible for many LGBTQ+ scientists to live openly without fear of job termination and workplace violence, reports indicate that queer people remain 17–21% underrepresented in STEM fields and are more likely to be professionally devalued, harassed, and career limited than their cisgender/heterosexual colleagues (Sci Adv 2022; doi.org/10.1126/sciadv.abe0933). Simply put, if we want to create equal opportunity for LGBTQ+ scientists, allyship requires action. Many LGBTQ+ scientists feel the need to hide their identity and self-police their behavior to avoid discrimination, which takes a toll on mental well-being. Isolation, mental health challenges, and poverty also harm career goals, limiting opportunities for training and advancement. Poverty disproportionately affects LGBTQ+ people, who are more likely to be homeless, are less likely to own their homes, and have no federal protection from housing and rent discrimination in the US (Williams Institute 2020; https://escholarship.org/uc/item/3cb5b8zj). There is no federal law preventing discrimination in insurance coverage and healthcare, and there is a shortage of physicians with sufficient training to treat transgender patients (South Med J 2021; doi.org/10.14423/SMJ.0000000000001261). One of the authors of this editorial found their postdoctoral job prospects limited by this shortage, as they had to pass on applying for ecology positions that were too remote from any trans-informed medical practitioners. Field research and travel can be especially difficult for queer scientists. When a person goes through a body scanner at the airport, security presses one of two buttons (male, female), and transgender people can face invasive pat-downs and harassment when their bodies do not match the assigned scan. At their research destination, queer ecologists may then be at risk from anti-LGBTQ+ laws or threats to their physical safety depending on the country, region, or state in which they conduct their work. We need concrete actions to address these barriers to daily life and work for queer ecologists, or we will continue to lose bright and enthusiastic scientists at early career stages. To support LGBTQ+ ecologists, we need to implement change across all scales of our work environments. At the broadest scale, funds should be set aside to address the barriers that LGBTQ+ ecologists face, including opportunities for grants and scholarships. For relocation, housing, and travel, institutions could provide funds in advance, as opposed to reimbursement: an unnecessary financial strain on minoritized graduate students. We need adequate access to healthcare, with coverage to seek LGBTQ+-informed healthcare. We also need data collection on LGBTQ+ scientists from institutions so that underrepresentation can be better studied and remedied. The US National Science Foundation's decision to ignore gender and sexual diversity in their workforce surveys’ demographic questions sets a concerning precedent (Open Letter 2023; https://bit.ly/3Zqpbi6). In workplace settings, normalizing (but not requiring) pronouns during group introductions is one basic action that can be adopted. Additionally, workplaces should require participation in in-person harassment and sensitivity training on a regular basis. There should also be systems in place for accountability and reporting of discriminatory incidents with external nonpartisan mediation as an option for complaints. At the interpersonal level, we advocate for support for your queer mentees and colleagues when they disclose an obstacle they’ve encountered, even and especially when their experience does not align with your own. You don’t have to understand our experience to respect and believe us. Prioritize the safety of researchers over data and other research projects. This applies to all safety concerns in field ecology but is particularly critical regarding identity-related safety issues. If you are a principal investigator of a lab that works in regions where it is illegal or unsafe to be LGBTQ+, it is your job to provide your queer students with good alternatives for equivalent fieldwork experience. Allyship is a continuous process made up of discrete actions and takes commitment to equity, compassion, and other core values. As academic ecologists, there are many demands on our time that are unpaid or contribute minimally to our advancement in highly competitive fields of work. For this reason, we must encourage institutions to better incorporate DEI work into evaluations of tenure and grant deliverables so that people continue to improve DEI because it is part of their job, it helps us do better science, and it is the right thing to do.

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)0,005
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,967
Score d'incertitude au seuil0,805

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,000
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 ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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 Opus0,191
Tête enseignante GPT0,444
Écart entre enseignants0,253 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2023
Routes d'admission1
Résumé présentoui

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