Code and Data for the Social Sciences: A Practitioner's Guide
Bibliographic record
Abstract
ion Rules (A) Abstract to eliminate redundancy. (B) Abstract to improve clarity. (C) Otherwise, don’t abstract. We are concerned about spatial correlation in potato chip consumption. We want to test whether per capita potato chip consumption in a county is correlated with the average per capita potato chip consumption among other counties in the same state. First we must define the “leave-out” mean of per capita consumption for each county: egen total_pc_potato = total(pc_potato), by(state) egen total_obs = count(pc_potato), by(state) gen leaveout_state_pc_potato = (total_pc_potato pc_potato) / (total_obs 1) We can now test whether pc_potato is correlated with leaveout_state_pc_potato. If so, we may need to adjust how we compute the standard errors in our model. We perform our analysis and are comforted to find little evidence of spatial correlation. But what if we are using the wrong level of aggregation? Maybe spatial correlation will show up at the level of the metropolitan area. Let’s copy and paste the code above and then adapt it to use metropolitan area instead of state as the level of aggregation:
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 | 0.032 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.020 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.486 | 0.412 |
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".