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Record W127515839 · doi:10.5751/es-02475-130214

What Is the Vulnerability of a Food System to Global Environmental Change?

2008· article· en· W127515839 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEcology and Society · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)Environmental changeEnvironmental resource managementFood systemsClimate changeVulnerability assessmentFood securityEnvironmental scienceGeographyEnvironmental planningEcologyComputer scienceAgriculturePsychological resilienceBiologyComputer security

Abstract

fetched live from OpenAlex

Assessing the vulnerability of broadly described food systems to global environmental change requires a new, synthetic approach. Food systems can best be conceptualized as the integration of humans and the environment or coupled social-ecological systems. However, much of the existing literature on vulnerability assessment focuses on either social or ecological systems, and conceptual gaps limit the holistic evaluation of linked systems in which both social and ecosystem outcomes are important. I suggest an approach with which to integrate factors across a food system to assess the system's vulnerability to environmental change by focusing on key processes and system characteristics. However, the multiple objectives of different actors in food systems make tradeoffs inevitable and complicate the evaluation of vulnerability. Further development and use of this approach is a promising avenue for future research because empirical evidence is needed to further elaborate these understandings.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.236
Teacher spread0.199 · 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