Approximating Cartography to the Customer's Expectations: Applying the “House of Quality” to Map Design
Bibliographic record
Abstract
The design of a map and guide for a Spanish natural park has been guided by the application of a product-development methodology known as quality function deployment (QFD). QFD is a tool for bringing the voice of the customer into the product-development process, from conceptual design to manufacturing. In order to develop a high-quality product whose design meets customers’ needs, market research has been developed to discover customers’ expectations and the strengths and weaknesses of competitors’ products. Sixteen main customer expectations (WHATs) were considered in relation to product comfort, content, and portrayal. In order to take into account the aforementioned expectations, 24 technical descriptors (HOWs) were considered. The product was finally specified by all the technical descriptors and their target values (HOW MUCHs). Results of the methodology are expressed using a set of matrices that depicts a house, the “House of Quality,” that concentrates the most important aspects of a product plan. Applying this methodology is an enriching experience, but somewhat difficult and time consuming.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".