Long-run Performance Following Cross-Listing: A Re-examination
Bibliographic record
Abstract
Selon les études antérieures, le rendement à long terme des titres qui s'inscrivent aux États-Unis (qui s'interlistent) est anormalement faible. Nous réexaminons ces résultats, qu'il est difficile de concilier avec les avantages procurés par cette opération et qui ne permettent pas d'expliquer le grand nombre d'interlistages observés récemment. Nous étudions la population des sociétés ouvertes canadiennes qui se sont inscrites aux États-Unis entre 1990 et 2005, en utilisant différentes méthodologies et indices. Une attention particulière est également portée aux désincriptions. En utilisant des méthodologies robustes, nous n'observons aucune performance anormale suite à l'interlistage des sociétés canadiennes. Nos résultats indiquent que les résultats antérieurs de sous performance à long terme pourraient provenir d'une combinaison de choix méthodologique et de biais de sélection et de survie. Ce document est une mise à jour de celui-ci publié en novembre 2007 sous le même numéro.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".