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Record W7015690051

Transforming public organizations into co-designing cultures : a study of capacity-building programs as learning ecosystems

2020· dissertation· en· W7015690051 on OpenAlexaboutno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2020
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningLiminalityOrganizational cultureSpace (punctuation)Organizational learningDependency (UML)Culture change
DOInot available

Abstract

fetched live from OpenAlex

When designers try to create lasting change in the public sector, their aim is not only to co-design meaningful new or improved services, but also to embed the capacity – rather than dependency – of co-design into the organization. Current research suggests that this embedded co-design capacity allows for ongoing transformation. Organizational change can be achieved in various ways, one of which is by facilitating experiential capacity-building programs that immerse public employees in codesigning methods and approaches over the course of several months. In this context, designers often experience that the existing organizational culture strongly constrains the adoption and application of new ways of working. However, many designers are not trained to address this cultural phenomenon.\nThrough a systems oriented design (SOD) approach, two cases of capacity building programs from different countries were analyzed, Fifth Space in Canada and Experimenta in Chile. An integrated research approach combining methods, such as research by design, gigamapping, interviews, and literature mapping was used to get new insights into the complex, contemporary design practice of nurturing and spreading organizational co-design capacities. The analysis of both programs drew my attention to the liminal space between the pre-existing culture in the organization and the emerging culture related to the introduction of new methods and ways of working. While it seemed like these conflicting cultures prohibited lasting innovation, there was also a lack of models and reflective tools toexamine these intercultural dynamics.\nThis thesis presents analytical and conceptual models that help to make interactions between the emerging and existing organizational culture more explicit and actionable. First, the Ripppling model provides three analytical dimensions – paradigm, practices, and the physical dimension to analyze the interactions between the emerging and dominant organizational cultures. This analysis can help to position the emerging culture in a constructive way without alienating the dominant culture, and to enable the co-existence of both for long-lasting transformational change. The Ripppling ecosystem model builds on the micro-interactions analyzed with the Ripppling model and proposes a system of embedded layers for large-scale cultural change processes that can have effects beyond the organization that participates in the capacity-building program. Taken together, the results of this thesis help to explain the difficulties public organizations face when introducing new capacities, such as codesign. My work suggests that these new capacities function as carriers orvehicles of cultural meaning that will inherently generate productive or unproductive tensions with the pre-existing culture. Therefore, one has to carefully recognize and address the underlying interactions across cultures to build organizational transformation strategically and to leverage the full potential of co-designing approaches. This work gives new insights into how to create continuous change in the public sector and has implications for future design practice, research, and education

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)0.018
metaresearch head score (Gemma)0.019
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 score0.020
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0200.031
Scholarly communication0.0160.011
Open science0.0030.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.289
Teacher spread0.242 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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