Narratives of Education and Constraint
Bibliographic record
Abstract
This chapter focuses on how different governance actors stress the constraining function of IIAs to achieve various goals and how they justify their actions. This chapter looks at how the IIA narratives have been used in reshaping national governance. This was chiefly through the narrative of IIAs as a disciplining and constraining force. We have identified that the general disciplining narrative about IIAs has three variants with different normative bents. These sub-types express how the governance actors evaluate the constraining potential of IIAs. On the one end of the spectrum, this disciplining effect may be viewed as flatly undesirable; on the other end, the constraint is viewed through a largely positive lens as a cultivating and educating force. Somewhat between sits the view of IIAs as simply something one must learn to live and deal with. Generally, the disciplining narratives about IIAs view IIAs as an incarnation of legal rationality superior to other rationalities, such as political or democratic rationality. Other considerations, even those pertaining to national constitutional arrangements, were cast in an inferior position and viewed as obstacles to a smooth implementation of IIAs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".