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Record W2034683335 · doi:10.1111/1467-9566.12097

How do values shape technology design? An exploration of what makes the pursuit of health and wealth legitimate in academic spin‐offs

2014· article· en· W2034683335 on OpenAlexafffundabout
Pascale Lehoux, Geneviève Daudelin, Myriam Hivon, Fiona A. Miller, Jean‐Louis Denis

Bibliographic record

VenueSociology of Health & Illness · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsÉcole Nationale d'Administration PubliqueInstitute of Health Services and Policy ResearchUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsCommitValuation (finance)Health technologyJudgementContext (archaeology)SociologyPublic relationsPositive economicsEconomicsBusinessPolitical scienceHealth careEconomic growthLawComputer scienceAccounting

Abstract

fetched live from OpenAlex

By actively supporting cooperation between academia, clinical settings and industry, several policy initiatives assume that the two policy agendas of health and wealth can be reconciled through the development of health technology. Our goal in this article is to shed light on the way the concurrent pursuit of health and wealth operates in practice by examining the valuation schemes, actions and decisions that shaped technology development in three Canadian spin-offs. Drawing on the sociology of judgement, our analytical framework conceives of technology development as a purposive collective action that unfolds in a normatively heterogeneous context (one pervaded with both corporate and public service mission values and norms). Our qualitative empirical analyses explore four valuation schemes and their corresponding regimes of engagement that characterise why and how technology developers commit themselves to addressing certain clinical, interactional, organisational and economic concerns throughout the development process. Our discussion suggests that the ability to reconcile health and wealth goals is to be found in the moral repertoires that provide meaning to, and render coherent technology developers' participation in corporate activities driven by economic growth.

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)0.050
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.374
GPT teacher head0.457
Teacher spread0.083 · 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.

Study designTheoretical or conceptual
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

Citations25
Published2014
Admission routes3
Has abstractyes

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