Is Conditionality Increasing or Diminishing?
Bibliographic record
Abstract
The implications of this last big shift in donors’ conditionality policies are controversial, and will be examined in more depth later in Chapter 7 of this study. In principle, however, the donor community is now strongly committed to reducing conditionality and enhancing ownership. For instance, the Commission for Africa (2005, p. 314) proclaimed that “policy conditionality…is both an infringement on sovereignty and ineffective.” The same year the United Kingdom produced an important policy document boldly committing the government to eliminate it and to adopt a non-interventionist approach: “The United Kingdom will not make our aid conditional on specific policy decisions by partner governments or attempt to impose policy choices on them (including in sensitive economic areas such as privatisation or trade liberalisation)” (DFID, 2005, p. 10). At their July 2005 meeting at Gleneagles, the G-8 leaders confirmed the need for recipient countries to “decide, plan and sequence their economic policies to fit with their own economic strategies for which they should be accountable to their people”. Such declarations have been become increasingly frequent since the second half of the 1990s, when disillusions with the results of Structural Adjustment became more widespread. Some countries such as Canada abandoned conditionality altogether. Over the last ten years, then, calls for the reduction of conditionality and enhanced ownership have gained momentum. But how much has really been achieved?
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".