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Record W4311691392 · doi:10.5463/thesis.14

Navigating Difference in Inter- & Transdisciplinary Learning

2022· dissertation· en· W4311691392 on OpenAlexaff
Annemarie Horn

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsAthena Sustainable Materials Institute
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsTransdisciplinarityContext (archaeology)SustainabilityEngineering ethicsDisciplineStakeholderSociologyStakeholder engagementInterdisciplinarityKnowledge managementPolitical scienceEngineeringPublic relationsSocial scienceComputer scienceGeography

Abstract

fetched live from OpenAlex

Complex sustainability issues – such as the energy transition and social inequalities – cannot be addressed from a single field. They are not confined to the disciplinary compartments into which academia is organised, and they are affected by and affect diverse societal actors. Therefore, such complex issues require inter- and transdisciplinary approaches in which scientists from diverse disciplines and societal actors collaborate and integrate their knowledge and perspective to generate novel insights. If we want to stimulate inter- and transdisciplinary collaboration for complex sustainability issues, this places a demand on our universities in how they train their students. And although the call for inter- and transdisciplinary approaches is commonly accepted, there are still many questions about how inter- and transdisciplinary learning comes about and can be supported. This motivated us to undertake this PhD project with the research question: How can we prepare university students for inter- and transdisciplinary approaches to address complex sustainability issues? We conducted an action research project in the context of a novel inter- and transdisciplinary master module that we designed and implemented as part of this research project: the Interdisciplinary Community Service Learning (iCSL) module. This module consists of two courses: iCSL1: Defining Challenges in a Multi-Stakeholder Context and iCSL2: Addressing Challenges through Transdisciplinary Research. Students from diverse fields collaborate with each other (interdisciplinarity) and with non-academic actors (transdisciplinarity). During the first two pilot years of the iCSL module (2019-2021), we collected research data for the six studies in this PhD project including observations, interviews, focus group discussions, and written reflections. From the data we learned that inter- and transdisciplinary collaboration requires that teams ‘navigate difference’ – in opinions, perspectives, and knowledge – to expose, recognize and confront differences and engage in knowledge integration. Only then the value of diverse knowledge and perspectives in cross-disciplinary teams can be used to generate novel, richer, more comprehensive insights and thereby realise the potential of inter- and transdisciplinarity. However, we found that the students in our courses often did the exact opposite; they tended to avoid, overlook, and ignore differences. Consequently, their collaboration ran the risk of remaining superficial and narrow, which prevented them from fulfilling the promises of inter- and transdisciplinary collaboration. The potential for inter- and transdisciplinary learning and training thus lies in supporting teams and individuals to navigate difference, counteracting personal and social tendencies. We saw that diverse representation – of disciplinary and societal perspectives - in the inter- and transdisciplinary collaboration were key to getting in contact with differences. Another condition turned out to be the collaboration towards a joint goal or product, that necessitated knowledge integration. Besides these conditions, we saw that interventions could support inter- and transdisciplinary learning. We found that it is important to engage the individual as a whole in inter- and transdisciplinary learning to stimulate the development of attitudes, behaviours and values, as opposed to an exclusively cognitive focus on knowledge and skills. Stimulating affective processes played an important role in doing so. Furthermore, we saw that students’ abilities to engage in inter- and transdisciplinary collaboration varied widely and that a combination of structure and freedom helped in catering to diverse competence levels and learning needs. Together, the findings from this PhD research project contribute to the understanding of inter- and transdisciplinarity and of inter- and transdisciplinary learning. Additionally, the research findings provides insight into processes that are relevant beyond the educational context when attempting to better understand and facilitate inter- and transdisciplinarity. In order to support the uptake of our lessons in other contexts, we also provide concrete recommendations and two directly usable, open access tools.

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)0.032
metaresearch head score (Gemma)0.031
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: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0080.016
Scholarly communication0.0190.015
Open science0.0030.034
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.086
GPT teacher head0.480
Teacher spread0.394 · 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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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Same topicInterdisciplinary Research and CollaborationFrench-language works237,207