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Record W2800331316 · doi:10.1108/ijmpb-03-2017-0022

Proposed framework for estimating effort in design projects

2018· article· en· W2800331316 on OpenAlexaff
Henrique Benedetto, Maurício Moreira e Silva Bernardes, Darli Rodrigues Vieira

Bibliographic record

VenueInternational Journal of Managing Projects in Business · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsComputer scienceDuration (music)Field (mathematics)Process (computing)Project managementKnowledge managementFocus (optics)Work (physics)EstimationInformation systemProcess managementEngineeringSystems engineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose a framework to assist in the estimation of effort for projects in the field of design. The study first seeks supporting material to outline an understanding of how design professionals have access to time estimation information for quoting their projects. Design/methodology/approach The work was based on in-depth interviews conducted with 13 professionals from various design sectors that focused on understanding important elements of the project quotation process. Content analysis was performed on the information provided, and four dimensions were identified. A framework that included these dimensions was designed and validated using a focus group composed of professionals involved in project quotation. The framework includes the generation of a project network structure; identifying tasks and their duration for each design activity; and the ways in which this information remains updated and evolves through the incorporation of dynamic systems concepts. Findings The results of this study will be the production of an external knowledge base that designers can use as a basis for performing their profession. Originality/value This study is relevant because there is no information source that addresses tasks and associated durations on which design professionals can rely for the development of quotations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.052
GPT teacher head0.339
Teacher spread0.287 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2018
Admission routes1
Has abstractyes

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