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Record W4404129011 · doi:10.5463/thesis.843

Experimenting for transformation

2024· dissertation· en· W4404129011 on OpenAlexaff
Mike Grijseels

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsAthena Sustainable Materials Institute
Fundersnot available
KeywordsTransformation (genetics)Computer scienceChemistry

Abstract

fetched live from OpenAlex

People with a disability face exclusion when trying to find employment and remain employed. A transformation towards more inclusive employment is long due. Increasingly actors in policy, researcher, industry, and citizens are exploring the potential of technologies for inclusive employment of people with a disability. However, the success of attempts at using technology for inclusion has been hit and miss. Part of the reason for this is the complexity and unpredictability of the problem. There are many different people involved in workplace exclusion who all might all represent different values and have different solutions for the problem. At the same time, it is unclear which technological innovations work well for inclusion and which ones don’t. In this thesis, I take this situation as a starting point for experimenting with different approaches for transformation. I take theoretical insights from fields like Disability Studies, Science and Technology Studies, and Actor-Network Theory and combine them with methodological insights from Reflexive Monitoring in Action and Situated Intervention. Building on notions from these fields, I analyse the setup a Learning Evaluation of 7 experimental pilots that all experiment with inclusive technologies. Taking on the role of reflexive- and situated monitor, I follow how these experiments attempt to navigate dichotomies, people coming together and forming associations, and the many insecurities and discomforts that come with aiming for transformation. In chapter 1, I introduce the notion of scripts; tracing the scripts and re-inscriptions that take place in the experiments to move beyond dichotomous understandings of disability and technology. In moving beyond the prosthetic-transformative dichotomy I show how shaping the socio-cultural environment around a technology is what matters for inclusion. In chapter 2, I reflect care practices that take place in the workplace. I show how care can take place in two layers of care; the care out there in the workplace and the care we as researchers bring. I then show how it is important to keep these layers in tension and how a position of being alongside can be helpful to do so. Moreover, I propose that being alongside can provide space for vulnerabilities and uncertainties of both researchers and stakeholders. In chapter 3, I show how practice-informed, empirical work can contribute to more broad scale transformation. I adopt insights from sustainability transitions and show how they can be enriched by a more in-depth empirical exploration of experiments. I introduce the notion of associations as a way to stay close to practice and show what is already working for inclusion in the workplaces. In chapter 4, I extend this notion further to the policy domain and show how one of the experiments face challenges when trying to scale-up what had worked in the experiment. I conclude that a pluralistic perspective is important in trying to transform practices, especially for allowing room for multiple action perspectives. In chapter 5, I experiment with novel ways of materializing technologies for inclusion. I follow the development of a socio-cultural manual, as a way to capture the sensibilities in from the first 4 chapters and help make this knowledge travel to other situations.

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)0.046
metaresearch head score (Gemma)0.108
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.108
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.015
Scholarly communication0.0060.010
Open science0.0030.008
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0330.004

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 head0.039
GPT teacher head0.434
Teacher spread0.395 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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