Don’t Blindly Use Data: Towards a Data Statement for Computational Financial Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, there has been a growing focus on the veracity of datasets. This concern has raised important questions such as: is the data appropriate for answering the research questions or hypotheses, is the data biased or harmful in any way, is the data quality data, and is there a sufficient understanding of the data for it to be used appropriately? We reviewed 46 papers from Google Scholar, IEEE, and ACM, and found that the majority of authors provide only a basic discussion of the dataset used in the research and do not address important issues such as potential bias or data that requires special attention. Following the work of Bender and Friedman, we propose a data statement framework specifically targeted to computational financial research to provide critical information to users. We also provide a completed data statement for published work as an example. This tool will help researchers provide users and stakeholders a better understanding of what comprises the data and provide an overview of what considerations were made in its creation. This will also help address any potential bias, errors or problems, and data that could be considered misleading in the context of the research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.038 | 0.038 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it