Improving the Effectiveness of Urban Projects
Bibliographic record
Abstract
E arlier chapters have reviewed the urban policy op- pilot program to rigorously evaluate selected early proj-Etions that are available to developing countries. A ects. Preparation for the evaluation began with approval variety of policies intended to alleviate urban problems of the first project in 1972, but it was mid-1975 before and improve the functioning of cities have been used or enough projects were ready for implementation. The proposed for use by developing countries. The results to housing program was launched with the assistance of date have been mixed and controversial, and there is an the International Development Research Centre (IDRC) emerging consensus that better evaluation of the actual of Canada. Programs in Senegal (the first project), El and possible outcomes of urban programs would be Salvador, the Philippines, and Zambia were selected for helpful to policymakers who must choose between poli- study, and annual conferences were held to discuss the cies. This chapter examines whether rigorous evaluation findings. The concluding conference for this phase of of projects can assist in improving the efficiency and the work, held in November 1980 in Washington, D.C., effectiveness of future urban policymaking and in for- was attended by project managers and researchers from mulating and implementing projects. Although much of the countries involved and from other interested coun-the chapter deals with specific shelter projects financed tries. Subsequently the evaluators ' attention has turned by the World Bank, the lessons drawn from such cases to the publication and dissemination of the programs'
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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 | 0.061 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".