Collaborating with Data Aggregators and the Estimize.com Setting
Bibliographic record
Abstract
ABSTRACT Our paper aims to assist researchers interested in generating new data and conducting field experiments to devise strategies for collaborating with startups and online platforms such as Estimize.com (Estimize). Specifically, we provide advice on collaborating with data aggregators in general and share past experiences working with Estimize, an online platform that crowdsources forecasts of earnings, revenue, key performance indicators (KPIs), and economic indicators. We inform academics about the opportunities and challenges of collaborating with online platforms such as Estimize by documenting prior successful and unsuccessful collaboration attempts and by sharing Estimize’s responses to our questions regarding what they deem important for collaboration. We also present details on the unique archival datasets currently available through Estimize, discuss important events impacting the platform, explain potential ways to generate new data by collaborating with the platform, highlight how the setting’s distinguishing features can help test accounting theories, and discuss limitations. Data Availability: Data are available from the public and proprietary sources cited in the text. JEL Classifications: M40; B40; C81; C90; C93.
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.
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 | 0.041 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".