Bibliographic record
Abstract
The measurement of household production is an exciting new field for empirical economic research and analysis. There is growing interest in research on macroeconomic importance of the value added by households using their own unpaid labor and their own capital. Governments in many countries (such as Australia, Canada, Finland, Germany, Italy, New Zealand, and Norway) have been providing millions of dollars for their national statistical offices to collect regular data on household time use. These data then help provide estimates of Gross Household Product (GHP), the value adde d by unpaid labor and household capital (Duncan S. Ironmonger 1996a, 2001). This chapter provides estimates of the value of GHP contributed by married households. The estimates could be called “Married Households GHP.” The chapter also provides estimates of the GHP produced by unmarried households. The GHP estimates are for Australia for the twelve months to June30, 1994, and are probably the first estimates for any country of the contribution of married households to household production. In macroeconomic terms, while married households were 63 percent of all households, they contained 74 percent of the adult population and produced 75 percent of GHP. MARRIED AND UNMARRIED HOUSEHOLDS DEFINED The criteria for distinguishing married households from unmarried households need to be determined before the household production accounts can be prepared. What definition of marriage should be adopted – legal or de facto? In keeping with the broad definition of marriage adopted by this book, married households include all households containing adult couples who state they are married, either legally or de facto.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".