Are stakeholders ready to transform phosphorus use in food systems? A transdisciplinary study in a livestock intensive system
Bibliographic record
Abstract
Food systems worldwide are vulnerable to Phosphorus (P) supply disruptions and price fluctuations. Current P use is also highly inefficient, generating large surpluses and pollution. Global food security and aquatic ecosystems are in jeopardy if transformative action is not taken. This paper pivots from earlier (predominantly conceptual) work to develop and analyse a P transdisciplinary scenario process, assessing stakeholders potential for transformative thinking in P use in the food system. Northern Ireland, a highly livestock-intensive system, was used as case study for illustrating such process. The stakeholder engagement takes a normative stance in that it sets the explicit premise that the food system needs to be transformed and asks stakeholders to engage in a dialogue on how that transformation can be achieved. A Substance Flow Analysis of P flows and stocks was employed to construct visions for alternative futures and stimulate stakeholder discussions on system responses. These were analysed for their transformative potential using a triple-loop social learning framework. For the most part, stakeholder responses remained transitional or incremental, rather than being fundamentally transformative. The process did unveil some deeper levers that could be acted upon to move the system further along the spectrum of transformational change (e.g. changes in food markets, creation of new P markets, destocking, new types of land production and radical land use changes), providing clues of what an aspirational system could look like. Replicated and adapted elsewhere, this process can serve as diagnostics of current stakeholders thinking and potential, as well as for the identification of those deeper levers, opening up avenues to work upon for global scale transformation.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".