COOKED
This catch-all category — demographers, criminologists, program evaluators, policy and behavioral researchers in agencies, think tanks, and consultancies — spends most hours on literature synthesis, survey instrument drafting, cleaning and coding datasets, running regressions in R/Stata/SPSS, and writing up findings in reports and briefs. Those are precisely the text-and-pattern tasks current models do at usable draft quality, and there is no license, stamp, or signature protecting the output. What persists is research design under ambiguity, primary data collection with human subjects, IRB-defensible methodology choices, and standing behind a politically contested finding in front of a board or legislature.
Mixed — a routine tier and a judgment tier. The recurring deliverables here — coding a 40,000-record ACS or NIBRS extract, drafting Likert batteries, running fixed-effects models and writing the 30-page evaluation report — are draftable by current models, and the 8 rather than 4 reflects the residual that isn't: specifying identification strategy when the treatment wasn't randomized, negotiating data-use agreements with agencies, and defending a sampling frame to an IRB that will ask why you excluded a subpopulation.
Some physical or field component. Most weeks are entirely laptop-and-VPN against restricted-use data enclaves, but the 5 covers the fieldwork tail — running focus groups, ride-alongs and site visits for a corrections or housing program evaluation, intercept surveys, and enumerator training that has to happen in the room.
No licence, no signature requirement. There is no licensing board for demographers or criminologists; a bad population projection or misspecified recidivism model produces a corrected erratum and a lost contract, not a suspension, and the 2 rather than 0 exists only because IRB approval and FISMA/Census DRB disclosure review put a named human on the certification.
Some relationship component. Agency clients and foundation program officers do re-hire the evaluator who understood their intervention last cycle, but the deliverable is a report that gets read by people who never met you, and much of the work arrives through RFP competition where the methods section, not the name, wins the bid.
Meaningful discretion. The 10 reflects calls that are genuinely yours — whether a null result gets reported as null, whether a subgroup n=61 is too thin to publish, how to describe a racial disparity in a policing dataset — but they land in a peer-reviewed or committee-vetted document with co-authors and a sponsor's review, not as a single unreviewable decision with someone's liberty or money attached.
Has AI actually changed your work?