United in Diversity? Disciplinary Normalization in an EU Project
Bibliographic record
Abstract
Managing diversity has emerged as a timely issue in organizations operating in the global economy. We contribute to the critical literature on diversity and its management in transnational organizations by exploring ways in which diversity is discursively (re)constructed in a European Union Framework Programme project. We draw on Michel Foucault’s insights on the specificity of the relations and mechanics of power, and the connections between disciplinary power, normalization and knowledge. We conceptualize the EU Framework Programme system as a disciplinary apparatus (dispositif)—a network of time-, place- and field-specific disciplining discursive practices—and approach diversity in an EU project as a technology of normalization. Managing diversity becomes thus understood both as an enabling and a limiting exercise of disciplinary power.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".