<scp>A</scp>chilles' heels of governance: Critical capacity deficits and their role in governance failures
Bibliographic record
Abstract
Abstract This article assesses the usefulness of conceptions of policy capacity for understanding policy and governance outcomes. In order to shed light on this issue, it revisits the concept of governance, derives a model of basic governance types and discusses their capacity pre‐requisites. A model of capacity is developed combining competences over three levels of activities with analysis of resource capabilities at each level. This analysis is then applied to the common modes of governance. While each mode requires all types of capacity if it is to match its theoretically optimal potential, most on‐the‐ground modes do not attain their highest potential. Moreover, each mode has a critical type of capacity which serves as its principle vulnerability; its “Achilles' heel.” Without high levels of the requisite capacity, the governance mode is unlikely to perform as expected. While some hybrid modes can serve to supplement or reinforce each other and bridge capacity gaps, other mixed forms may aggravate single mode issues. Switching between modes or adopting hybrid modes is, therefore, a non‐trivial issue in which considerations of capacity issues in general andAchilles' heel capacities in particular should be a central concern.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".