Bibliographic record
Abstract
Abstract I provide a summary, reflection and assessment of the current state of economic development in both the policy and academic worlds. In terms of development policy, currently, the primary focus is on policy interventions, namely, foreign aid, aimed at fixing the “deficiencies” of developing countries. Academic research also has a similar focus, except with an emphasis in rigorous evaluation of interventions to estimate causal effects. A standard set of versatile quantitative tools is used, e.g., experimental and quasi‐experimental methods, which can be easily applied in a range of settings to estimate the causal effects of policies, which are typically presumed to be similar across contexts. In this article, I take a step back and ask whether the current practices are the best that we can do. Are foreign aid and policy interventions the best options we have for poverty alleviation? What else can be done? Is our current research strategy, characterized by rigorous but a lack of context‐specific analysis, the best method of analysis? Is there a role for other research methods, for a deeper understanding of the local context and for more collaboration with local scholars?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".