Decolonizing and re-rooting soil science: Towards equitable global collaboration and local empowerment, case of Morocco
Bibliographic record
Abstract
Global scientific collaboration is essential for advancing soil science, yet persistent structural inequities, deeply rooted in colonial legacies, undermine its potential. This paper critically examines the dynamics of international research partnerships in Morocco, highlighting imbalances in research funding, authorship, and data sovereignty. While agroecological movement, global soil mapping and remote sensing technologies offer promise, their accuracy and applicability often depend on local data and expertise, which are often marginalized in externally driven top-down projects. Case studies reveal patterns of "helicopter or parachute research," where foreign teams extract data without meaningful local engagement, reinforcing epistemic hierarchies. Commercial digital agriculture ventures further exacerbate these issues through proprietary, non-transparent models that lack local contextual calibration. Meanwhile, Moroccan soil science remains fragmented, with weak institutional coordination and limited research prioritization. To foster equitable collaboration, this study advocates for structural reforms: prioritising local leadership, ensuring ethical data governance, and strengthening national research ecosystems. By situating Morocco within broader debates on decolonizing science, this work contributes to pathways for inclusive, equitable, sustainable, and mutually beneficial partnerships in environmental research. Also, by scaling soil intelligence using its own scientific progress and national schemes, the country can preserve its agricultural base, strengthen its climate and environment commitments, and position itself as a continental leader in digital and autonomous soil research by sharing developed knowledge and technologies. • Moving beyond data availability requires fostering balanced collaboration and inclusive co-creation in soil research. • Open data alone does not ensure equitable access; research participation and benefits must be fairly distributed. • Advancing global soil science requires digital literacy and fair, transparent data governance in a fast-changing world. • Embracing a human-centric and problem-solving approach enhances soil research impact for current and future needs. • Strengthening accountable programs and modern curricula empowers and attracts the next generation of soil scientists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".