Some like it complex: building a common multidisciplinarity background from local experiences within the South-Mediterranean environmental research communities
Bibliographic record
Abstract
This article addresses the difficulty of introducing and establishing multidisciplinarity in environmental research within and among the South-Mediterranean environmental research national communities. Moreover, this work attends to assess the internal and external structural factors treating such complex issues in rural, urban, and peri-urban contexts as well as the connections and dependencies of these factors. Throughout a series of programs, projects, and actions that involved scientists and scholars from Algeria, France, Lebanon, Morocco, and Tunisia, some common patterns can be observed despite notable differences in environmental and political contexts. Thus, the main common issues involve funding matters (budget reductions and less versatility), administrative and social hierarchy, relatively small connections with public services and community representatives, and finally the reluctance shown by many researchers to make data available for the community. Nevertheless, the fact that national and international (Arabic and French speaking sphere) researcher's communities have progressively built mutual knowledge thanks to different collaborations is a major achievement, sustaining multidisciplinarity in environmental research. Indeed, this allowed the elaboration of sustainability metrics, demarches, and procedures for assessing environmentally and socioeconomically complex issues.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".