Bibliographic record
Abstract
The debate over whether urbanization and related socioeconomic developments affect large-scale surface climate trends is stalemated with incommensurable arguments. Each side can appeal to supporting statistical evidence based on data sets that do not overlap, yielding inferences that merely conflict with but do not refute one another. I argue that such debates can only be resolved in an encompassing framework, in which both types of results can be demonstrated on the same data set, in such a way that apparent support for one conclusion occurs as a restricted case of a more general specification that supports the other, and where the restrictions can be tested. The issues under debate make such data sets challenging to construct, but I give two illustrative examples. First, insignificant differences in warming trends in urban temperature data between windy and calm conditions are shown in a restricted model whose general form shows temperature data to be strongly affected by local population growth. Second, an apparent equivalence between trends in a data set stratified by a static measure of urbanization is shown to be a restricted finding in a model whose general form indicates significant influence of local socioeconomic development on temperatures.
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 | 0.093 | 0.420 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".