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Record W4312578962 · doi:10.46692/9781529216677.007

Emergent Tensions in Diversity and Inclusion Work in Universities: Reflections on Policy and Practice

2022· other· en· W4312578962 on OpenAlexaboutno aff
Samantha Brennan, Gwen E. Chapman, Belinda Leach, Alexandra Rodney

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Inclusion (mineral)Work (physics)Political scienceSociologyPedagogyEngineering ethicsPublic relationsSocial scienceEngineeringLawMechanical engineering

Abstract

fetched live from OpenAlex

Introduction Amid growing population diversity, universities face the challenge of including historically under-represented students, staff, and faculty (Kobayashi, 2009; Tienda, 2013; Karakasoglu, 2014; Henry et al, 2017). While diversity can be mandated and is a precursor for inclusion, inclusion is a normative target stemming from voluntary action and requiring complex reorganization, which presents challenges for universities (Tienda, 2013). As Winters puts it, inclusion work involves ‘creating an environment that acknowledges, welcomes and accepts different approaches, styles, perspectives and experiences, so all can reach their potential and result in enhanced organizational success’ (Winters, 2014, p 206). This work demands answers to pressing questions: how do universities create an inclusive environment? How do we transform our institutions? This chapter examines a collaborative workshop designed to mobilize knowledge about ‘doing diversity and inclusion’ that was organized jointly by the University of Guelph, Canada, and Bremen University, Germany, two institutions with a long-standing strategic partnership. The workshop brought together 50 students, staff, administrators, and faculty to share practical actions for better serving under-represented community members. The universities share societal positioning, size, and many research interests, as well as a commitment to changing university structures to foster inclusion. Over the course of the workshop, interesting insights emerged, including differences in priorities and histories, structure, and strategy, producing an opportunity for constructive and creative thinking about how to address shared and divergent aspects of inclusion. The presentations and conversations that filled our two days together vividly described the lived experience of doing diversity and inclusion work. The workshop was designed to include people doing the work from different perspectives, holding different positions in relation to diversity and inclusion policies and practices. Participants ranged from those who are the objects of policies to those who set policy, enact it, advocate for it, work against policy, struggle with it, and work outside it. While the workshop was intended to generate a framework for creating sustainable inclusion strategies, and to identify and develop recommendations for policies and practices, it raised substantially more questions than answers and revealed tensions that emerge when doing diversity and inclusion work on university campuses. These tensions serve as the context in which students, staff, faculty, and administrators must work to advance inclusivity agendas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.091
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.091
Threshold uncertainty score0.484

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.007
Science and technology studies0.0520.124
Scholarly communication0.0510.046
Open science0.0070.050
Research integrity0.0220.019
Insufficient payload (model declined to judge)0.0070.001

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 head0.170
GPT teacher head0.389
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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