Performance Implications of Using Signaling and Screening for Expanding Interfirm Business Networks: Evidence from Franchising
Bibliographic record
Abstract
43 p. ; Includes bibliographical references (pp. 33-38) ; Authors "thank three anonymous reviewers, Josef Windsperger, William Allender, Kersi Antia, Charles Ingene, Robert Palmatier, Sourav Ray and Ruhai Wu for their constructive comments on previous versions of this paper. This paper has benefited from feedback received during presentations at the 2018 EMNet conference, the 2017 ISOF conference, the 2017 ETSymposium on Marketing Strategy, the ASAC 2017 conference, the 2018 Winter AMA conference, and research seminars at McMaster University, Indian Institute of Management (Bangalore) and Indian Institute of Management (Ahmedabad). This research is supportedby funding from the Social Sciences and Humanities Research Council of Canada. Funding for this research was provided by the Institute for the Study of Business Markets of the Pennsylvania State University."
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".