Rethinking Socio-Ecological Relations from Inclusion and Exclusion: A New Approach to Socio-Environmental Conflicts
Bibliographic record
Abstract
This article aims to explore how to extrapolate and integrate the conceptual frame of inclusion/exclusion in a socioeconomic sense to the study of socio-ecological conflicts. It emerges from the similarities between both studies and the major contribution that these concepts have made in the socioeconomic understanding of poverty and deprivation. Poverty and deprivation are fait accompli, but the same deprivations can be the result of a great variety of exclusionary processes; to truly attend to it, it’s necessary to understand the relational aspects that led to those outcomes (exclusion/inclusion). The analysis of socio-ecological conflicts follows a similar culminating bias: analyzing results without deepening the relational aspects of the process that leads to them; therefore, it’s proposed to use these concepts to enhance its analysis. To achieve it, a profound literature review was carried out regarding the frameworks used to address socio-ecological issues and inclusion/exclusion. Finding that not only the concepts are satisfactory to this analysis, but that they even permit making joint analyses between socioeconomic exclusion/inclusion and socio-ecological inclusion/exclusion. This also enables a new focus for environmental policy from the study on new inequalities.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.012 | 0.085 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.010 |
| 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".