Building a prospective participatory approach for long-term agricultural sustainability in the Lezíria do Tejo region (Portugal)
Bibliographic record
Abstract
Addressing the gaps between theory, research and practice, this paper explores a hybrid mindset of participatory action research (PAR), geoprospective and participatory geographical information system (PGIS). This approach brings together stakeholders, policy-makers and researchers – in an agricultural peri-urban region of Portugal, the Lezíria do Tejo region – to anticipate the possible changes in agricultural territories, while taking spatial dynamics into account. It uses a four-step methodology which integrates qualitative and quantitative approaches to select stakeholders’ interview areas, implement prospective workshops to engage and explore the stakeholders’ interests and encourage actions towards finding solutions for long-term agricultural sustainability in this region. The results from our study highlight that more participative approaches such as the ones developed here must be implemented towards decision-making, since they help to dispel the distrust between stakeholders, strengthen community cohesion and also contribute to build common solutions drawing upon various perspectives. From a PAR perspective, this work contributes to bridge the gap between academia and practitioners, as is shown by a willingness of the practitioners to actively participate in the research under progress.
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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 | 0.027 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".