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
Abstract I examine whether a strategic focus on data analytics is associated with improvements in firms' internal information quality. Using textual analysis of firm disclosures to identify a data analytics strategy, I first document that firm, leadership, and operating environment characteristics are all important determinants of the decision to adopt a data analytics strategy. I next use operating and financial reporting outcomes to infer whether a data analytics strategy improves internal information quality. I find that a data analytics strategy is associated with enhanced operating efficiency, as adopting firms invest and utilize existing resources more efficiently. I also find that a data analytics strategy is associated with more accurate management forecasts. These results, collectively, are consistent with a data analytics strategy improving firms' internal information quality. Lastly, I corroborate and extend my findings with job postings data, and the results suggest that firm leadership signals their support for data analytics initiatives through disclosure.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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