Bibliographic record
Abstract
Abstract Social constructivism has increasingly been seen as one of the chief theoretical contenders in contemporary scholarship in international relations. As a research program, one of its main substantive contributions to the field has been to show that moral norms — and thus ethics — matter in world politics. In this very agenda itself, constructivist scholars have embodied ethical commitments — at its most basic level this most often has been one of challenging realist scepticism concerning the possibilities for progressive moral change. Yet the plausibility of such ethical positions has typically been defended by constructivists on rigorous empirical terms — showing that human rights norms or norms of warfare can matter, for example — rather than on comparably rigorous normative grounds (that such norms are ethically desirable). This article briefly outlines the trajectory of the constructivist research programme, arguing that its development and responses to its critics have now led it — and its challengers — centrally to explicit engagement with ethical questions. It then considers the extent to which constructivism can be said to entail a distinctive ethic at all, and outlines its potential contributions to addressing global ethical challenges.
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 | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.099 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".