Lucrative Startups Screening for Seed Accelerators: A Data-Driven Selection Criteria Pipeline
Bibliographic record
Abstract
As startup accelerators become more prevalent, a greater number of startups seek such programs to accelerate their growth. Thus, the staggering number of applicants in each round of intake has made the process of selecting the most lucrative startups costly and overwhelming. This thesis proposes a framework that infers features with the highest prediction power in lucrative startup selection. First, the study extracted 35 criteria with respect to early-stage start-ups. Then, leveraging graph analytics techniques, it extracted another 12 features with respect to the interaction of startups as members of a cohort. I extensively studied the effectiveness of the proposed pipeline and criteria on 35,647 companies founded between 20122015 as well as 763 startups admitted to accelerators in the same period. Results show that the proposed pipeline can predict the success of the final admitted startups with a high performance in terms of AUC (88%) and F1-score (79%).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".