BIG DATA: Preconditions to Productivity
Bibliographic record
Abstract
The continuous waves of data flooding our world have created a phenomenon that has attracted businesses to leveraging this information. According to overall research the sheer volume of data generated, stored, and mined for consumer transactions insights has become economically relevant to businesses success, governmental operations, and consumer savvy transactions. The history of previous trends in IT investment and innovation and its impact on competitiveness and productivity strongly suggest that Big Data can have a similar power, namely the ability to transform our lives. The same preconditions that allowed previous waves of IT-enabled innovation to power productivity, i.e., Technology innovations followed by the adoption of complementary management innovations, are in place for Big Data, vendors and suppliers of Big Data technology and advanced analytic capabilities to have at least as much ongoing impact on productivity as vendors and suppliers of other kinds of technology.
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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".