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
Data has been called the new oil. Despite of its high value, only one percent of the world's data has been extracted for its insights. Data analytics is currently available only to the technical elite of data and IT experts, completely out of reach for general users. End users today only have indirect access to insights, completely dependent on this technical elite. However, no exponential growth in the population of IT and data experts is fast enough to meet the demand of volume and speed of data analytics to go beyond today's one percent of data analyzed. We are far from the ultimate data utopia in which data analytics is provided as a utility, available to and accessible by general users in real time. This paper introduces experience-based analytics: analytics for end users in accessible forms that serve their data analytics requirements as individuals in real time, in a manner that is contextually relevant, transparent with cognitive insights and anticipation. The notion is for the data and IT experts to re-think from delivering analytics results to users as a final form of consumption, to a new paradigm in which general users are enable to perform their own analytics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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