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Record W4411139225 · doi:10.63315/jrcd.v20i2.2616

Fueling Communities into the Future: A Survey Of Firewood Banks to Identify Strengths, Weaknesses, Opportunities, and Threats To Organizational Sustainability

2025· article· en· W4411139225 on OpenAlexvenueno aff
Sarah Butler, Eric E. Griffith, Richard E. Harper, Jessica Leahy, Brett Butler, C. E. Hart, Jason E. E. Dampier

Bibliographic record

VenueJournal of rural and community development · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsnot available
FundersUniversity of Massachusetts AmherstU.S. Forest ServiceNational Institute of Food and AgricultureNorthern Research StationMaine Agricultural and Forest Experiment StationU.S. Department of Agriculture
KeywordsFirewoodSustainabilityStrengths and weaknessesBusinessEnvironmental resource managementEnvironmental planningEconomicsGeographyEcologyPsychology

Abstract

fetched live from OpenAlex

Firewood banks are community-driven initiatives that aim to reduce fuel poverty by providing firewood to households facing heating insecurity. As firewood bank expansion continues, there is an emergent urgency to understand their operations, processes, capacities, and challenges. We formally surveyed known firewood bank leaders and evaluated the results through the framework of a strengths, weaknesses, opportunities, and threats (SWOT) analysis to better understand the strengths, weaknesses, opportunities, and threats of firewood banks more broadly. Further understanding of firewood bank attributes within a SWOT framework will provide leaders, policymakers, and outreach professionals with valuable insights to assess support needs and ensure long-term organizational sustainability in fuel-poor communities. The results of this research underscore the successes these organizations have achieved, the challenges they may face in the future, and highlight critical areas for future research. Keywords: wood bank, community resources, SWOT analysis, fuel poverty, local resources ______________________________________________________________________________ Alimenter les communautés vers l’avenir : Une enquête des banques de bois de chauffage pour identifier les forces, les faiblesses, les opportunités et les menaces vers la durabilité organisationnelle RésuméLes banques de bois de chauffage sont des initiatives communautaires qui visent à réduire la précarité énergétique en fournissant du bois de chauffage aux ménages confrontés à l'insécurité thermique. Alors que l’expansion des banques de bois de chauffage se poursuit, il devient urgent de comprendre leurs opérations, leurs processus, leurs capacités et leurs défis. Nous avons formellement interrogé les dirigeants connus des banques de bois de chauffage et évalué les résultats dans le cadre d’une analyse des forces, faiblesses, opportunités et menaces (SWOT) afin de mieux comprendre les forces, les faiblesses, les opportunités et les menaces des banques de bois de chauffage de manière plus générale. Une meilleure compréhension des attributs des banques de bois de chauffage dans un cadre SWOT fournira aux dirigeants, aux décideurs politiques et aux professionnels de la diffusion des informations précieuses pour évaluer les besoins de soutien et assurer la durabilité organisationnelle à long terme dans les communautés pauvres en ombustible. Les résultats de cette recherche soulignent les succès obtenus par ces organisations, les défis auxquels elles pourraient être confrontées à l'avenir et mettent en évidence les domaines critiques pour les recherches futures. Mots-clés : banque de bois, ressources communautaires, analyse SWOT, précarité énergétique, ressources locales

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.111
GPT teacher head0.398
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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