Literature overview of three directions of research to assess the future success of an IT start-up at the early-stage phase
Bibliographic record
Abstract
Tento ÄlĂĄnek systematicky uspoĹĂĄdal roztĹĂĹĄtÄnou literaturu do ucelenĂŠho pĹehledu a poskytuje kompaktnĂ informace o kvalitativnĂch kritĂŠriĂch zamÄĹenĂ˝ch na budoucĂ ĂşspÄch IT start-upu v ranĂŠ fĂĄzi vĂ˝voje. MÄĹĂtkem ĂşspÄchu je pĹijetĂ vĂ˝znamnĂŠ VC investice. Tyto informace mohou odhadci nebo analytici zohlednit pĹi sestavovĂĄnĂ finanÄnĂho plĂĄnu (investiÄnĂho modelu). Z reĹĄerĹĄe literatury vyplynuly tĹi smÄry vĂ˝zkumu v oblasti oceĹovĂĄnĂ start upĹŻ. SouÄasnĂĄ oceĹovacĂ praxe disponuje propracovanĂ˝mi metodami oceĹovĂĄnĂ veĹejnĂŠho kapitĂĄlu, jakĂ˝mĹž jsou zralĂŠ spoleÄnosti, zatĂmco se na oceĹovĂĄnĂ rizikovĂŠho kapitĂĄlu nahlĂŞà jako na tĂŠma zahalenĂŠ tajemstvĂm a Äernou magiĂ. PrvnĂ smÄr rozvĂjĂ a prohlubuje metody oceĹovĂĄnĂ veĹejnĂŠho kapitĂĄlu. DruhĂ˝ a tĹetĂ smÄr rozvĂjĂ oblast oceĹovĂĄnĂ rizikovĂŠho kapitĂĄlu (tj. VC investic) a hledĂĄ souvislost mezi pĹijetĂm VC investice a samotnĂ˝mi faktory, kterĂŠ vedly k pĹijetĂ VC investice. ZĂskĂĄnĂ VC investice v druhĂŠm smÄru pĹedstavuje kritĂŠrium ĂşspÄchu, zatĂmco tĹetĂ smÄr analyzuje jednotlivĂŠ faktory vyskytujĂcĂ se v ĂşspÄĹĄnĂ˝ch start upech.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 teacher head, 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".