Bibliographic record
Abstract
A new mode of inquiry, problem solving, and decision making has become pervasive in our society, consisting of applying computational, mathematical, and statistical models to infer actionable information from large quantities of data. This paradigm, often called Big Data Analytics or simply Big Data, requires new forms of data management to deal with the volume, variety, and velocity of Big Data. Many of these data management problems can be described as data curation. Data curation includes all the processes needed for principled and controlled data creation, maintenance, and management, together with the capacity to add value to data. In this talk, I describe our experience in curating some open data sets. I overview how we have adapted some of the traditional solutions for aligning data and creating semantics to account for (and take advantage of) Big Data.
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 | 0.059 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.008 | 0.028 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.026 |
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".