Livestock Management, Masculine Care and Cognitive Ecology: An Ecomasculine Approach to John Connell’s The Farmer’s Son
Bibliographic record
Abstract
Ecological masculinism has been considered as a parallel movement to ecofeminism. It intends to investigate the role played by men in spatially coexisting with nature. Furthermore, it meditates on envisioning how men belonging to various categorical masculinities, nurture the natural environment in augmenting atmospheric stability. As a domain, ecological masculinism eschews the hegemonic approach towards nature by establishing a symbiotic relationship with biotic and abiotic components in an ecosystem. In compliance with the literary sphere, this movement embodies certain malestream norms which appeal to ecological specifications such as the insightful practice of Earthcare, ensuring ecological integrity, and regenerating natural resources. This paper examines John Connell’s The Farmer’s Son: Calving Season on a Family Farm (2019), an eco-autobiographical memoir as a frame of reference to spotlight certain eco-masculine intertextualities such as animal husbandry, glocal sustainability and psycho-cognitive objectives. This paper employs the theoretical frameworks of Karla Elliott’s Caring Masculinities and Susan Signe Morrison’s Waste-ern Tradition in mapping the eco-masculine praxis embedded in the prototypic conceptualisation of ‘eco-man’s husbandry’, through the rational quotients of livestock management, masculine care and cognitive ecology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".