How the feedback of audiences advantages fast learners: evidence from Canadian software firms from 2004-2013
Bibliographic record
Abstract
How the Feedback of Audiences Advantages Fast Learners: Evidence from Canadian Software Firms from 2004 to 2013 David Maslach, Florida State University Chengwei Liu, University of Warwick Rod McNaughton, University of Auckland Slow learning – low sensitivity to recent performance – has been argued to be essential for adaptation in uncertain environments because learners do not converge to local maxima. Using a simulation model, we demonstrate that fast learning can outperform slow learning when the role of audience feedback is incorporated. A differential sampling process determines how audiences form expectations and react to performance deviations, and potentially reward fast learners for unreliability. We empirically examine our argument using the Google Search data regarding the Canadian software firms from 2004 to 2013. The results support that fast learners tend to attract more (less) attention when outperforming (underperforming) the expectations than slow learners. More generally, we present a sampling account for why fast learning is a sensible strategy, even with uncertain outcomes.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".