Bibliographic record
Abstract
Analytics (BA) remains an important research topic for the coming year. Many elements contribute to a successful BA solution. Within the business analytics team at IBM, we are looking closely at the following trends: Business Analytics Marketplace: Buyers of Analytics software are changing. Purchasing decisions are no longer made exclusively by Information Technology departments. End users have a greater role in BA than ever before. Business Intelligence: Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making -- Source: Forrester The heart of a Analytics solution is Intelligence (BI). Reporting and analysis are the main features visible to end users. Under the covers, BI is undergoing the biggest transformation since its inception over 40 years ago. Data warehouses are now more dynamic and timely. In many cases, these reporting mainstays are being replaced by ad hoc systems that can report over operational data. The shift toward Software Oriented Architecture (SOA) continues with the Cloud often being the ultimate destination.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".