Bibliographic record
Abstract
With this study we are the first to systematically compare today’s two major counterpartsas a source of accounting and financial data for researchers: CompustatNorth America by Standard and Poor’s and Worldscope by Thomson Financial. Thisinvestigation is conducted for U.S. and partly Canadian data over an extensive periodfrom 1985 to 2003. We examine more than 650 data items available in bothdatabases and address the question of whether or not the decision for one or theother source may have an impact on the outcome of research projects. It is probablycommonly assumed that this impact is minor, but it also leaves room to questioncertain results. We show that the use of both databases should lead to comparableresults, but also find that if, e.g. a size bias, is not treated with care the quality ofresults may differ considerable. Furthermore after 1998 the number of firms coveredby Worldscope exceeds the one covered by Compustat by about one fourth.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.304 | 0.188 |
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".