Assessing and controlling the quality of a project end product: the earned quality method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quality is achieved to the extent that a project end product meets the client's needs and expectations. This paper addresses the fundamental issues relating to the periodic assessment and control of the quality of the end product of a project. The proposed earned quality method (EQM) enables project managers to assess and control the quality of the end product throughout the project's life cycle. EQM rests on two fundamental assumptions: (1) that quality is a measurable concept; and (2) that quality is accrued progressively throughout the project's life cycle. EQM decomposes the end product's overall quality into its main attributes and criteria and relates them to the project activities. This elucidation process of the client's needs and expectations helps both the client and the project manager to identify valid quality indicators, estimate their relative contribution to the overall quality objective, and devise acceptable assessment protocols. Using a multicriteria approach, EQM allows project managers to deal in a formal and quantitative fashion with the client's stated and implied needs. By comparing earned quality and planned quality of the work performed. EQM enables project managers to detect quality deviations and initiate early corrective actions. EQM should contribute significantly to the quality of a project end product by improving communications between the client and the project manager at its outset, by elucidating the client's needs and expectations, by providing ongoing quality assessment measures, by avoiding time-consuming and costly rework through early corrective actions, by promoting greater quality accountability and project coordination, and finally, by preventing costly legal disputes over the quality of the project end product.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it