Theory, change and the search for epistemological courage in shaping a new world order
Bibliographic record
Abstract
No matter how narrowly you focus your spatial or temporal lenses, you are bound to catch sight of multiple significant challenges to human community. Many of these challenges are shared, such as Covid-19, though their impacts on individuals and groups are felt unevenly. Some challenges are immediate and existential, such as the wars in Ukraine, Syria, and Yemen. Others, such as race, gender, caste, and class-based inequalities, are deeply embedded in social structures, providing privilege and persecution, and reward and oppression in unequal measures. And climate change, though slower moving, holds out the prospect of leading to total social collapse. How to make sense of these dramatic changes? This essay explores the adequacy of theories of IR and G/IPE in explaining the emergent world (dis)order. It argues that, whether orthodox or critical, theory must find a way to centre humanity within the biosphere if theory is to adequately inform practice.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.013 | 0.210 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".