Taking the First Steps beyond GDP: Maryland’s Experience in Measuring “Genuine Progress”
Bibliographic record
Abstract
Gross Domestic Product’s (GDP) limitations as a prosperity indicator are now widely recognized, leading to a search for “beyond-GDP” alternatives. The US state of Maryland has calculated one such alternative, the Genuine Progress Indicator (GPI), since 2010. What effect is Maryland’s GPI having in practice? Is there any evidence to date that the GPI has shaped policy and public priorities in ways that live up to its supporters’ hopes—whether for a transformative shift beyond the economic-growth paradigm or simply better policymaking? What key obstacles exist to fulfilling those goals? This article draws on semi-structured interviews with elite respondents—including Maryland’s former governor, senior public servants, academics, non-governmental organization employees and foundation leaders—involved in producing, advocating and using the GPI, along with analysis of relevant documents. Although significant impacts on policy are not yet evident and a change of governor has removed high-level support, the GPI initiative has revealed innovative possibilities for more environmentally and socially minded policymaking and introduced new ideas with potential long-term impacts. However, various challenges remain, including strengthening the political constituency behind the GPI, more deeply embedding it into the policymaking process and addressing the GPI’s own limitations in supporting a beyond-GDP economic narrative.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".