Discovering Hidden Truths in Marketing Phenomena: can the Data Percolation Methodology Help?
Bibliographic record
Abstract
Se presenta la metodología de percolación de datos que permite descubrir las realidades subyacentes ocultas en los comportamientos inconscientes de los agentes de mercado; éstos incluyen el hurto y el uso ilícito de los bienes de empresa. Las metodologías tradicionales se muestran muy ineficaces para descubrir estos comportamientos. Este artículo compara dos estudios realizados en el ámbito de las relaciones comerciales, uno por Anderson y Narus (1990) y el otro por el autor para demostrar que la percolación de datos es un método apropiado cuando se trata de entender las motivaciones de los agentes del mercado. Los resultados demuestran la existencia muy fuerte de depredación percibida por parte de ellos.
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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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".