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Record W2314195665 · doi:10.1386/tmsd.9.2.149_1

Collaborative lifecycle design A viable approach to sustainable rural technology development

2010· article· en· W2314195665 on OpenAlexaff
Israel Dunmade

Bibliographic record

VenueInternational Journal of Technology Management and Sustainable Development · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMaintainabilityBusinessSystem lifecycleProcess managementProcess (computing)Sustainable developmentKnowledge managementProduct lifecycleComputer scienceMarketingNew product development

Abstract

fetched live from OpenAlex

The purpose of this article is to present a lifecycle design methodology for rural technology development that promotes stakeholders' participation throughout the developmental process. The model was based on findings from rural development studies, several years of experience in rural technology development and on lifecycle management principles. This article explained what collaborative lifecycle design for rural technology development is, and how it can be implemented. It also described how stakeholders involved rural technology design and development framework was used in developing low-cost, potable multi-purpose threshers. The performance characteristics of the machines developed through the use of this methodology were within the same functional performance range with imported models. In addition, the use of the methodology resulted in lower production cost, better acceptance and improved maintainability. It also fostered rapport between stakeholders, led to attainment of self-reliance in that technology instead of dependence on imported machinery and improved our post-harvest technology capacity building. Moreover, it led to the development of a technology that is socio-culturally compatible and environmentally friendly.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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 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

Citations11
Published2010
Admission routes1
Has abstractyes

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