The social cost of labor in rural development: job creation benefits re‐examined
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Job creation effects are examined as they would apply to social analysis of rural development programming by public or private sector agencies. A synthesis and critique are provided of approaches to valuing the social opportunity cost of labor. These approaches vary according to whether or not unemployment is present in the pre‐project state and according to whether or not there is interregional migration in response to project hiring. Graphical, partial equilibrium analysis illustrates why, in general, job creation and project employment give rise to social costs, not benefits. The magnitude of these social costs is shown to depend upon the presence of payroll taxes, wage subsidies and unemployment, in addition to the market's supply and demand elasticities. These social costs may be reduced or offset in specific instances where projects increase the value of labor's productivity or reduce its costs, such as with job training, worker mobility and skill development projects. Careful attention to these approaches can help society choose correctly among alternative development proposals and among alternative (labor‐intensive versus capital‐intensive) technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it