Bibliographic record
Abstract
Part One Performance of IPOs 1 Nasdaq IPO around the market peak in 2000 2 Returns to style investments in Initial Public Offers 3 The long-run performance of Taiwan's IPOs conditioning characteristics: evidence from efficiently learning Anlin Chen, Sue L. Chiou and Chinshun Wu 4 Short and long-run performance of IPOs traded on the Istanbul stock exchange Mehmet Orhan 5 Indexing the IPO sector with IPOX (TM) indices Josef A. Schuster 6 The size effect of firm's going public on the Spanish capital market Susana Alvarez-Otero and Victor M. Gonzalez-Mendez 7 Earnings management and the lomg-run performance of Spanish inital public offerings Maria J. Pastor-LLorca and Francisco Poveda-Fuentes 8 IPO Intial returns on European new markets Giancarlo Giudici and Peter Roosenboom 9 Efficiency of U.S. Internet IPOs: a data envelopment analysis approach Greg N. Gregoriou and Maher Kooli Part Two IPO Underpricing: International Evidence 10 Generalzing the winner's curse hypothesis: the case of the Belgian IPO market Michel Boelen and Georges Hubner 11 Flipping activity in fixed offer price mechanism allocated IPOs Dimitrios Gounopoulos 12 Getting the IPO right: viva la France? Edel Barnes 13 Underpricing and the aftermarket performance of inital public offerings: the case of Austria Wolfgang Aussenegg 14 The hot-issue period in Germany-what factors drove IPO underpricing? Stefan Guenther and Marco Rummer 15 Reassessing Canadian IPO underpricing: evidence from common share, capital pool company and unit offering Maher Kooli 16 IPO Underpricing and ownership structure: evidence from the Istanbul stock exchange M. Banu Durukan Part Three Corporate Strucuture and IPO Evaluation 17 IPOs and earnings management in Germany Tereza Tykvova 18 Signaling and the valuation of IPOs: regression tests Steven Xiaofan Zheng 19 The role of venture capitalists in IPO performance: emprical evidence from Germany Andreas Hack and Erik E. Lehmann 20 Ownership structure and intial public offerings in Portugal Jose Miguel Almeida and Joao Duque Part Four Bookbuilding, Listing and Underwriting 21 Bookbuilding and shre pre-allocation in IPOs Nancy Huyghebaert and Cynthia Van Hulle 22 Costs and benefits in the choice of the audit and underwriting qualitiy in the IPO market: an empirical analysis of competing theories Joseph Aharony, Ran Barniv and Chan-Jane Lin 23 Siamese Twins and Virtual Mergers: Dual Listed Companies in Australia Paul Ali 24 Equity issuance trends in Australia's listed investment fund Martin Gold 25 Do underwriters create value for issuers by subjectively determining offer prices? Steve Dolvin
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".