Bibliographic record
Abstract
This paper aims to addresses three questions related to conceptual design: 1) Why is environment important? 2) What is environment? and 3) How to analyze environment? Unlike product-, function (affordance)-, or user-centered methodologies, it is argued that the known environment of a product to be designed must be the first thing to be looked at in a conceptual design. The environment can be categorized into three types: the natural, the built and the human. A complete set of environment for a design is implied in the lifecycle of a product to be designed, which can be further defined by lifecycle activities in terms of time. The analysis of environment consists of three activities: 1) determine decision directions by asking design questions based on a design problem statement; 2) clarify the design problem statement by answering questions following the decision direction; and 3) formulate an environment system from a design problem statement. The process is driven by asking the right questions by analyzing the ROM diagram of the design problem statement. The answers to the questions follow the lifecycle analysis of the product and its environment components. This paper completes a detailed procedure for Environment Analysis, which is the one of the three activities in Environment-Based Design (EBD).
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.013 | 0.038 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| 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".