Too much power or no power: when does intermediary’s power result into better wine and happier farmers?
Bibliographic record
Abstract
The study analyzes the trading relationship performance between farmers and intermediaries and the factors shaping it, with a focus on intermediary’s power, based on a structured survey of vineyard farmers in Kosovo. Confirmatory factor analysis is employed to develop measures for the study latent variables, and ordinary least squares regression is used to test the hypothesis. To further validate the results, machine learning (i.e. random forest) is used to model the factors affecting the relationship performance between farmer and intermediary. The results show that when the intermediary has considerable (excessive) power, it leads to low trading relationship performance with farmers. Also, when the intermediary has little power, the relationship performance with farmers behaves in a similar way. The main contribution of this paper is to further illuminate the debate on the role of power in business-to-business relationships, in that it points out an alternative explanation; stating that there is an optimal level zone that power needs to exist, in order to achieve above average trading relationship performance. Outside this zone, either low or excessive/high intermediary’s power results in poor relationship performance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".