Herkömmliche Methoden und Instrumente für die frühen Phasen der Produktentstehung
Bibliographic record
Abstract
Im vorangegangenen Hauptkapitel wurden die generellen Anforderungen und Aufgabenstellungen des FFE sowie vorliegende Erkenntnisse über dessen Erscheinungsformen und Erfolgswirkungen dargestellt. Die Ausführungen gleichen hierbei in einem Punkt vielen Arbeiten über die frühen Phasen der Produktentstehung: Sie beschreiben das FFE und geben generelle Empfehlungen für seine Gestaltung, thematisieren konkrete Instrumente und Methoden24 zu seiner Unterstützung jedoch nicht. Bislang existieren in der Literatur damit kaum Instrumente und Methoden, die speziell für den Einsatz im Fuzzy Front End konzipiert wurden - wohl nicht zuletzt, weil die frühen Phasen der Produktentwicklung aufgrund ihrer geringen Formalisierung und Strukturierung als „unmanagebar” gelten, [vgl. Murphy, Kumar: Canadian Survey 1997, S. 5].
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".