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Progressing Gender Inclusion in the ADF

2021· article· en· W7014727726 sur OpenAlexaboutno aff

Notice bibliographique

RevueUNSWorks (University of New South Wales, Sydney, Australia) · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueGender, Security, and Conflict
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Filter (signal processing)Work (physics)Point (geometry)NucleofectionDemotion
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This report provides a framework for the development of gender inclusion indicators for the ADF. The draft indicators were developed through an examination of academic and grey literature, and are underpinned by complementary reports written by the research team. While diversity in the workplace is about the presence or absence of people from a variety of backgrounds (especially historically marginalised groups), inclusion refers to individuals’ capacity to fully participate and to influence decisions. Much of the literature tends to agree that inclusion has two dimensions: belongingness and uniqueness (Mor Barak, 2015; Jansen et al., 2014). Fostering a sense of belonging is essential, but if this means that employees have to give up their unique characteristics, they are not experiencing true inclusion (Mor Barak, 2015). In the context of the ADF as a workplace, the aggressive socialisation and modification of identity for ADF members on joining represents a threat to inclusion. However, military training and socialisation is also an opportunity, as it provides an intervention point where the ADF can adjust training regimes to explicitly move towards the valuing of difference. The adoption of inclusive behaviours can then be furthered through taking up and promoting inclusive leadership. The authors have drawn on research conducted by the US Army to identify the elements of inclusive leadership, and how these can be measured. Generally, ‘gender inclusion’ is not a recognised concept in either grey or academic literature (see Appendix A for relevant terminology and definitions). However, researchers (Kossek et al., 2017) have identified elements of gender inclusion, which are: fairness and anti-discrimination for women in work access, process, and outcomes; leveraging women’s talents; and workplace support for women. As discussed below (p.8), antecedents are required for a gender inclusive workplace. Researchers and consultants have identified the elements of broader inclusion (not specifically focused on gender). While these bundles of behaviours vary between researchers, common elements include psychological safety, involvement in the work group, feeling respected, having a voice in the organisation, and having access to leaders (Shore et al., 2018; Taylor, 2019). Considerations around inclusion also need to encompass men’s resistance to gender equality. A great deal of research has been conducted on how to overcome male resistance (for example, see Dover et al., 2020; Pease, 2008). Empowering men to treat everyone fairly within a culture of inclusion requires long-term interventions, based on education and activities to counter stereotyped associations and to support becoming an ally (Dover et al., 2020). Inclusive leadership training and engaging middle managers can also increase ownership of initiatives to effect behavioural change (Gartner, 2019; Colley, Williamson and Foley, 2020). We provide existing models of diversity and inclusion which could be adapted to the ADF context (see Figure 1, p.9 and Appendices C and D). We also suggest inclusion indicators for the short- and longer-term (see pages 14-16), and suggest new indicators (see pages 16-17. We also consider how current indicators used in the Women in the ADF report can be enhanced (see Appendix B). Measuring gender equality and diversity largely relies on quantitative measures; inclusion, which relates to people’s feelings and experiences, is usually assessed through subjective measures (often with a qualitative component). Inclusion can be invisible to people who already experience it, because it is the absence of exclusionary events. Therefore, creating bespoke measures of inclusion needs to be done in partnership with as diverse a group of the workforce as possible (Gaudiano, 2019). Researchers have also recommended that organisations develop not only inclusion indicators, but inclusion competency indicators. These operate at the intrapersonal, interpersonal, group and organisational level. These competencies could be measured as a proxy for inclusion and measured through a survey. Essentially, these competencies are all elements of inclusive leadership, and a range of inclusive leadership surveys already exist. This report complements the recent report produced by the authors benchmarking gender equality in the ADF against other militaries and like organisations. It also complements the forthcoming program logic report, as indicators outlined in this report may also be captured in the program logic. Synergy between gender equality indicators in the program logic report and inclusion indicators requires further consideration, and consultation across the three Services to ensure they are fit for purpose. As noted by the Canadian Armed Forces (2020), inclusion is very sensitive to context and hence any inclusion indicators will need to be carefully developed in partnership with the ADF. Any such undertaking would necessarily be a large-scale project. The US Army has developed a methodology for developing leadership indicators to enable leadership surveys to be conducted (Ratcliff et al., 2018). The methodology used was onerous, rigorous and time consuming, as detailed. Further development of gender inclusion indicators could form Phase 2 of the Defence Gender Research Program.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,197
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)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,120
Tête enseignante GPT0,315
Écart entre enseignants0,196 · 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'étudeQualitatif
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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