MétaCan
Menu
Back to cohort
Record W2611124129

Rethinking the marginal: service design for development

2016· article· en· W2611124129 on OpenAlexfundno aff
Satu Miettinen, Hanna-Riina Vuontisjärvi

Bibliographic record

VenueLaCRIS (University of Lapland) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
FundersInternational Development Research CentreRobert Gordon University
KeywordsService (business)Computer scienceBusinessMarketing
DOInot available

Abstract

fetched live from OpenAlex

This article is asking how service design can be used for development and improving livelihoods in the margin? The margin can be geographical but is can also be created around complex wicked problems that have resulted from historical or societal reasons. The article presents two cases studies where the margin is constructed around two different situations. The first case is looking at the arctic context of Lapland where geography causes marginalization and isolation. This case study is connected with “IKÄEHYT” project focusing on designing services with the elderly. The project was run in 14 Lappish communities to increase accessibility, inclusion and wellbeing. The second case is looking at indigenous San communities in South Africa where historical and societal context is creating a challenging situation for the local youth. The second case study is presenting ”PARTY” project (Participatory Development) with youth where service design is used to increase the youth participation in open democratic society and in designing their services.<br/>The article has a strong social design ethos. The article gives a brief overview on social design debates and uses that as a framework to analyze the case studies. Both case studies contribute to the rethinking of the marginal, how it is constructed and how service design can be used to create new solutions to overcome the challenges.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

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

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 head0.046
GPT teacher head0.204
Teacher spread0.158 · 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 designNot applicable
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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueLaCRIS (University of Lapland)Same topicInnovation and Socioeconomic DevelopmentFrench-language works237,207