Bibliographic record
Abstract
This chapter aims at examining the main statistical issues that any researcher faces when generating and eventually analyzing a harmonized data set. We first present the challenges faced when combining the data. Three steps must be taken when combining data sets. We must select the appropriate data and measures. We must decide which analytical procedure(s) to use and, once these decisions have been taken, it is necessary to make sure that the analyses performed on the harmonized data set will not lead to biased results. How we deal with these issues often determines what we can – or cannot – do with the data set afterward. We tackle three issues that stem from analyzing harmonized data sets, that is dealing with time, with missing values, and with weighting. This chapter shows that there are so many differences between survey projects, including the questions asked, the way they are asked, and where and when they are asked, that any analysis has to carefully assess how these different aspects interact with each other before reaching firm conclusions on specific effects.
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 | 0.664 | 0.793 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.017 | 0.015 |
| Research integrity | 0.013 | 0.069 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".