Proposed framework for estimating effort in design projects
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".