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Record W7014727726

Progressing Gender Inclusion in the ADF

2021· article· en· W7014727726 on OpenAlexaboutno aff

Bibliographic record

VenueUNSWorks (University of New South Wales, Sydney, Australia) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Filter (signal processing)Work (physics)Point (geometry)NucleofectionDemotion
DOInot available

Abstract

fetched live from 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.315
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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